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Journal of Geographical Sciences >

2016 , Vol. 26 >Issue 3: 339 - 354

DOI: https://doi.org/10.1007/s11442-016-1272-5

Orginal Article

Progress on quantitative assessment of the impacts of climate change and human activities on cropland change

  • SHI Xiaoli , 1, 3 ,
  • WANG Wei 1, 3 ,
  • Shi Wenjiao , 2
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Author: Shi Xiaoli, PhD and Associate Professor, specialized in climate change. E-mail:

*Corresponding author: Shi Wenjiao, PhD and Associate Professor, E-mail:

Received date: 2015-09-08

  Accepted date: 2015-10-20

  Online published: 2016-07-25

Supported by

National Natural Science Foundation of China, No.41401113, No.41371002, No.41471091

The Science and Technology Strategic Pilot of the Chinese Academy of Sciences, No.XDA05090310

The Key Project of Physical Geography of Hebei Province

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Journal of Geographical Sciences, All Rights Reserved
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Abstract

It is important to study the contributions of climate change and human activities to cropland changes in the fields of both climate change and land use change. Relationships between cropland changes and driving forces were qualitatively studied in most of the previous researches. However, the quantitative assessments of the contributions of climate change and human activities to cropland changes are needed to be explored for a better understanding of the dynamics of land use changes. We systematically reviewed the methods of identifying the contributions of climate change and human activities to cropland changes at quantitative aspects, including model analysis, mathematical statistical method, framework analysis, index assessment and difference comparison. Progress of the previous researches on quantitative evaluation of the contributions was introduced. Then we discussed four defects in the assessment of the contributions of climate change and human activities. For example, the methods were lack of comprehensiveness, and the data need to be more accurate and abundant. In addition, the scale was single and the explanations were biased. Moreover, we concluded a clue about quantitative approach to assess the contributions from synthetically aspect to specific driving forces. Finally, the solutions of the future researches on data, scale and explanation were proposed.

Key words： climate change; human activities; contributions; quantitative; cropland pattern

Cite this article

SHI Xiaoli , WANG Wei , Shi Wenjiao . Progress on quantitative assessment of the impacts of climate change and human activities on cropland change[J]. Journal of Geographical Sciences, 2016 , 26(3) : 339 -354 . DOI: 10.1007/s11442-016-1272-5

1 Introduction

Cropland is essential for food production, and also the largest man-made landscape among land use types (Shi et al., 2014). Spatial-temporal changes of cropland can largely influence agriculture’s ecosystem services, such as grain yield and biodiversity (Müller et al., 2013). Cropland pattern is sensitive to climate change and other natural factors. Additionally, human activities are also closely related to cropland distributions. Due to the limited area of cropland and the increasing population, identifying the contributions of driving forces of cropland change has become a hot spot in the fields of land use and land cover change, agriculture and food security. Cropland changes are affected by climate change and human activities simultaneously. The quantitative assessment of the contributions of climate change and human activities to cropland spatial-temporal changes is a scientific topic both to the fields of climate change and land surface system sciences.
In recent years, many researchers assessed the impacts of climate change and human activities on cropland change and made great progress (Shi et al., 2014; Lambin and Meyfroidt, 2012; Liu et al., 2009; Ye et al., 2012). Climate change is not the only factor to drive cropland change; human activities (socio-economic factors, policy, etc.) also affect the cropland distribution largely (Shi et al., 2014; Ye et al., 2012). Lambin and Meyfroidt (2012) found that ecological feedbacks seem to better account for cropland reclamation in Vietnam, while economic factors better explain reforestation. Liu et al. (2009) reported that cropland dynamics are driven by policy, economic development and climate warming in China. In the past 300 years, the contradiction between limited land and rapidly increasing population was intensified by extreme climatic disasters in North China. The resultant large-scale reclamation of Northeast China led to the formation of an organic chain of climate-policy-reclama
In the previous studies, combined effects of climate change and human activities were considered synthetically. However, based on the two-variable or multivariable data, most of the studies analyzed the causal relationships between cropland change and driving forces qualitatively. Identifying the contributions of climate change and human activities to cropland change from quantitative aspects can help us to take appropriate actions to intervene and adapt to climate change, which aimed to support rational land use and balance the ecosystem services. Therefore, the contributions of climate change and human activities to cropland change are needed to be assessed quantitatively. In this study, we presented an overview of the methods that identifying the contributions of climate change and human activities to cropland change at quantitative aspects. The progress and the defects in the previous researches were also introduced. Finally, we discussed the perspectives on future researches. This review can provide references for cropland conservation, food security ensuring and climate change adaptation.

2 Methods

Quantitative methods for identifying the contributions of climate change and human activities to cropland change include five main types, i.e. model analysis, mathematical statistical method, framework analysis, index assessment and difference comparison (Table 1).
Table 1 Methods for identifying the contributions of climate change and human activities to cropland change
Methods Study areas Periods Models Literatures
Model analysis China 2000-2005 CLUE-S, Dinamica EGO Gao and Yi, 2012
Europe 2000-2050 ROIMPEL, SFARMMOD Audsley et al., 2006
Global 2000-2080 AEZ, BLS Tubiello and Fischer, 2007
Vietnam 2007-2030 CLUE, MAGNET Rutten et al., 2014
Mathematical statistical method Jamaica 1942-2010 Logistic regression Newman et al., 2014
Mississippi, USA 1938-2010 Stepwise logistic regression Schweizer and Matlack, 2014
Taibus Banner, China 2008-2009 Logistic regression Hao et al., 2010
Jiangsu Province, China 2000-2008 Principal component analysis, linear regression Du et al., 2014
Amazon, Brazil 2001-2012 Fixed effects panel regressions Gollnow and Lakes, 2014
Fuyang City, China 1999-2006 Random effects model, fixed effects model Zhong et al., 2011
Ireland 1993-2007 Random effects model, spatial autoregressive random effects model Upton et al., 2014
Bohai Rim 2010 Linear regression, spatial error model, spatial lag model, geographically weighted regression model Wu et al., 2014
Jiangxi
Province		 1988-2005 Mechanism model of land conversion Zhan et al., 2010
China 1996-2011 STIRPAT model Zhang and Chen, 2014
Framework analysis Swiss 1930-2000 System definition, system analysis, and system synthesis framework Hersperger and Bürgi, 2009
Portugal,
Sweden		 1950-2010 Pressures-Frictions-Attractors-Triggers Beilin et al., 2014
Index assessment Northern Iran 1967-2002 Land use change area ratio Kelarestaghi and Jeloudar, 2011
Swiss Alps Past 120 years Rates of landscape change Schneeberger et al., 2007
Difference comparison China 1980-2000 Constant eco-region in the 1980s Gao and Liu, 2006
Sahel 1982-2003 Residuals trend of NDVI
(NDVI RESTREND)		 Herrmann et al., 2005
South Africa 1985-2003 Rain-Use Efficiency, NDVI RESTREND Wessels et al., 2007
Ordos region, China 1981-2000 NPP RESTREND Xu et al., 2009

2.1 Model analysis

Model analysis is one of the classical methods to study the land use and land cover change. It is helpful to well understand the process and mechanism of cropland change. Conversion of Land Use and its Effects Model (CLUE), Slope, Land use, Exclusion, Urban extent, Transportation, Hill shade Model (SLEUTH) and Cellular Automata and Spatial-temporal Markov chain Model (CA_Markov) are the representative models in common use (Yu et al., 2011). In this method, the contributions of climate change and human activities are identified through the cropland pattern simulation. For example, CLUE-S and Environment for Geoprocessing Objects Model (Dinamica EGO) were used to investigate the contribution of each driver of cropland change in China for the period 2000-2005 (Gao and Yi, 2012). In addition, some researchers combined models of land use and farm decision-making to emphasize the importance of human decision-making. Audsley et al. (2006) employed the crop yield model (ROIMPEL) and the Silsoe Whole Farm Model (SFARMMOD) to estimate the climate change consequences of cropland.
The contributions of climate change and human activities to future cropland change can be assessed by the difference of cropland pattern simulated by the land use model and economic model (Tubiello and Fischer, 2007; Rutten et al., 2014). First, the contribution of socio-economic factor can be evaluated by projecting the future cropland distribution under the economic scenario only, then the future pattern of cropland is projected under both the economic scenario and climate change scenario, contribution of climate change can be evaluated through the difference between the cropland distribution simulated with and without climate change. Coupling an Agro-Ecological Zone (AEZ) model with Basic Linked System (BLS) model, global cropland distributions were simulated with and without climate change over the period 1990-2080, then the contributions of climate change and human activities to cropland change were quantified by Tubiello and Fischer (2007). In Vietnam, Rutten et al. (2014) combined a Modular Applied GeNeral Equilibrium Tool (MAGNET) with a spatial land use allocation model (CLUE) to analyze future land use pattern with and without climate change scenarios for the period 2007-2030, and then investigated the relative role of climate change and human activities. Due to the simulation of the potential pattern of current and future cropland, model analysis becomes a powerful tool to investigate the transformation of cropland system (Tang et al., 2009). However, application of these models has been largely restricted to the difficulty of parameter acquisition, the sophisticated objective conditions, the absence of validation and criterion, and the hypothesis limitation.

2.2 Mathematical statistical method

Mathematical statistical method is mostly used to analyze the contribution of specific factors from climate change and human activities. The frequently used methods are logistic regression (Newman et al., 2014), principal component analysis (Du et al., 2014), panel regression (Gollnow and Lakes, 2014) and other mechanism statistical models (Zhan et al., 2010; Zhang and Chen, 2014).
Logistic regression is an appropriate tool to analyze the binary dependent variable, and often used to study the driven mechanism of cropland change. The contribution of independent variable is explained by the odds ratio or the marginal effect of the regression. Some are also determined by the hierarchical partitioning analysis, i.e. the difference between the equations with or without certain independent variable (Prishchepov et al., 2013). Currently, most of the studies investigated the time-series relationships between dependent and independent variables at the regional scale. For example, Newman et al. (2014) determined the climate change and socio-economic drivers of cropland reclamation by using logistic regression in the Cockpit Country, Jamaica. Schweizer and Matlack (2014) used stepwise logistic regression to separate the driving forces’ influences in land use change of the coastal plain of Mississippi, from 1938 to 2010. Some logistic regressions were based on panel data, which were mostly from household surveys. Hao et al. (2010) used household surveys and binary logistic model to analyze the driving factors of cropland transfer due to differences between farmers in Taibus Banner of Inner Mongolia, China. Nevertheless, the above-mentioned studies can only tell the dependent variables’ dependency to independent variable, the explanation of causal relationships still needs the aid of relevant theory. Thus, some scholars selected the co-integration test and Granger causality test to solve it. For Changsha-Zhuzhou-Xiangtan urban agglomerations of China, the internal relationships between the cropland quantity and the major driving forces were verified by the co-integration test and Granger causality test analyses (Liu et al., 2010).
The most obvious problem in regression analysis is the co-linearity among variables (Corbelle et al., 2012). Researchers are increasingly combining stochastic sampling, correlation coefficient testing, ridge regression and principal component analysis to solve the problem (Du et al., 2014; Zhang et al., 2012). Du et al. (2014) identified the drivers’ relative importance of land use change in Jiangsu Province by principal component analysis and general linear model. Principal component analysis summarizes the information from the independent variables effectively, but ill-conceived of the interpretation.
Due to the consideration of variables’ heterogeneity and co-linearity eliminating, the panel regression analysis can deal with the dynamic phenomenon better than the time-series data or panel data. For example, fixed effects panel regression was employed to quantify the contribution of cattle and soy production with cropland abandonment in Brazil between 2001 and 2012 (Gollnow and Lakes, 2014). However, the traditional panel regression is deficient in spatial correlation and spatial dependence. Spatial panel regression, which is connected with the dynamic econometric model, including the individual, time and spatial factors, can overcome the false hypothesis and estimation deviation of the model. Thus it is becoming a new choice to explore the attribution of cropland change. Focusing on Ireland, a random effects and a spatial autoregressive random effects model were employed to identify the significant effect of physical, economic and policy factors on cropland conversion (Upton et al., 2014). Wu et al. (2014) constructed linear regression model, spatial error model, spatial lag model and geographically weighted regression model to explore the relationships between cropland distribution and driving forces in the Bohai Rim.
Furthermore, some scholars developed mechanism statistical models to reveal the sophisticated relationships between climate change, human activities and cropland transformation. Zhan et al. (2010) developed an econometric model to explore the driving mechanism of cropland conversion from 1988 to 2005. Based on Stochastic Impacts by Regression on Population, Affluence and Technology Model (STRIPAT) and socio-economic development data of China from 1996 to 2011, the marginal contributions of urbanization process, population, economic development, and technical factors on cropland change were assessed by Zhang and Chen (2014).
Owing to the feasibility and availability, mathematical statistical method is widely used in exploring the attribution of the cropland change. However, the hypotheses between driving forces and cropland change are too simple, and understandings of cropland response to climate change and human activities are still incomplete. These may lead to the contradictions between co-linearity, autocorrelation, non-standardization, comprehensiveness and rationality. By way of mathematical statistical method, we can obtain the contributions of specific factors (such as temperature, precipitation, economic and population) to cropland change, but cannot get the spatial distribution difference of these contributions. Furthermore, this method is incapable of distinguishing the integrated contribution of climate change or human activities.

2.3 Framework analysis

Framework analysis, stemmed from general system theory, is also called conceptual model. Drivers-Pressures-State-Impact-Responses (DPSIR) is the most widely used system framework analysis (Shiferaw, 2011). Based on the framework, a list of driving forces is selected. The authors explore the relationships between driving forces and land use change on the basis of document analysis (i.e. the studies, chronicles, cantonal reports, and archival records from governments); expert interviews are made in order to supplement the document analysis. According to the impacts on land use change, each driving force is assigned a value from 1 to 0. These values are summed up to the important value to determine the contribution. For example, Benini et al. (2010) took DPSIR framework to distinguish the contributions of main factors acting on the cropland conversions of Lamone river basin in Northern Italy.
In addition, Bürgi et al. (2004) proposed system definition, system analysis, and system synthesis framework. The system definition includes defining the study area, the achievement of the study, the spatial-temporal resolution, and the landscape elements of interest. The system analysis focuses on three parts, i.e., the change and persistency of physical landscape elements, the actors and institutions, the driving forces. The actors, institutions, and driving forces are linked through causal relationships in the system synthesis phase, and their influences on the land use are determined. Based on this framework, Hersperger and Bürgi (2009) built the importance value to quantify the relative importance of socio-economic, political, cultural, technical and natural driving forces of urbanization, agricultural intensification and greening, from various administrative levels and time scales.
Then Slatmo (2011) proposed pressures, frictions, attractors and triggers framework. Pressures are factors that are forcing stresses on land use, such as political, economic, cultural and technical. Frictions are factors that prevent change: resisting, slowing down or changing the direction of land use change. Attractors are site physical characteristics. Triggers are factors that spur land use change in a direct, immediate ways (e.g. the opening of a new road). Beilin et al. (2014) estimated the relative importance of international, national and local drivers to cropland abandonment based on this framework in Portugal and Sweden. Compared to other methods, framework analysis has profound theoretical background, and interprets the causality more comprehensively and reasonably. Nevertheless, this method is mostly based on the indicator calculation; the obvious subjectivity may inevitably exist in the weight selecting.

2.4 Index assessment

The authors often select direct indicators representing land use change (rates of landscape change or land use change area ratio), they identify the driving forces (political, economic, cultural, technical and natural) and actor levels (international, national, canton, municipality, planning agency, organization, group, individual and farmer level) to impact on land use change. The interviews, which include free discussion and systematical thoughts, are then taken with farmers, politicians, planners and historians, interviewees are shown graphs with the time-series indicators of land use change. Additionally, historical documents are analyzed in order to supplement the interview. Finally, the contributions of driving forces to land use change are investigated. For instance, in northern parts of Iran, the land use change area ratio was computed to determine spatial patterns of land use changes in relation to physical and socio-economical factors by Kelarestaghi and Jeloudar (2011). Schneeberger et al. (2007) reconstructed the rates of landscape change in northern fringe of the Swiss Alps, expert interviews with farmers, politicians, planners and historians helped in identifying the contributions of actors and driving forces to land use change. This method is easy to operate, and can assess the contributions of climate change and human activities to cropland pattern quantitatively. Unfortunately, the spatial difference cannot be reflected in these studies.

2.5 Difference comparison

The difference comparison method is usually used to distinguish the integrated role of climate change and human activities in cropland conversion. First, some indirect indicators are selected, such as Normalized Difference Vegetation Index (NDVI) or Net Primary Productivity (NPP), the potential value of the indirect indicator is simulated with climate change only. Then the differences between observed and potential value of indicator are considered as human activities’ impact, the contributions in various periods can be implied by difference trend of these indirect indicators. Based on the constant eco-region in the 1980s, Gao and Liu (2006) analyzed the respective impact degree and direction of changes caused by climate change and human activities to land use in China. This practice inspires the researches on the contribution of cropland change, while the spatial differences of driving forces may be ignored.
Residuals trend of NDVI method (RESTREND) can separate the contributions of climate change and human activities to cropland pattern in arid and semi-arid zones. Furthermore, the contributions can be displayed spatially. The process is that, based on the highly correlated relationships between vegetation and rainfall in arid and semi-arid zone, the regression equation is constructed to estimate the NDVI. It is hypothesized that, the difference between observed and predicted NDVI can be considered as the human impact. Herrmann et al. (2005) used this method to investigate the ‘human signal’ to the cropland change in the Sahel. Moreover, Rain-Use Efficiency (RUE= NPP/Rainfall or ΣNDVI/Rainfall) can also imply the cropland degradation. Wessels et al. (2007) tested the RUE and RESTREND to detect the human-induced land degradation in South Africa, results indicated that the RESTREND showed better. The RESTREND method is mainly suitable to arid and semi-arid zones. In such regions, the vegetation growth is correlated with precipitation. So the key issue of this method is to validate the relationships between precipitation and NDVI. Nevertheless, the vegetation is not always significantly related to the precipitation everywhere. Except for the precipitation, other factors, such as temperature and soil quality, should also be involved (Wessels et al., 2007).
In Ordos region of China, Xu et al. (2009) selected potential NPP and the difference between potential and actual NPP to analyze the relative roles of climate change and human activities in sandy desertification, respectively. Based on the remote sensing images, this method can spatially identify the contribution of land use for a long time and multi-scale analysis. However, the exact causes of the negative trend, e.g. overgrazing by livestock or cultivating, should be explored by the aid of widely field investigation and higher-resolution remote sensing image at the local scale.

3 The contributions to the cropland pattern change

3.1 Climate change

Climate change can substantially induce the variation of regional hydrological cycle and environment, affect cropping systems, crop productivity and land use, subsequently causing considerable variability of cropland pattern (Newman et al., 2014; Chen et al., 2012; Piao et al., 2010; Dong et al., 2009). The cropland in Northern China, which is limited by heat, had benefited from the climate warming. The cropping center of rice in Northeast China was 128°52′E, 45°37′N in 1970 and 129°53′E, 46°29′N in 2006, and extended northward about 80 km (Chen et al., 2012). Climate warming had already caused a significant northward expansion of rice cropping boundaries from ~48°N to ~52°N in Heilongjiang Province, the areas extended from 0.22 Mha in the early 1980s to 2.25 Mha in 2007 (Piao et al., 2010). With the increasing of annual accumulated temperatures ≥10 °C since the late 1980s, 31.6 Mha of land were transferred from the spring wheat zone to the winter wheat zone (Dong et al., 2009). In mountainous areas, temperature change affects the cropland distribution because of the terrain variation (IPCC, 2014). The gravity center of China’s cropland was gradually moving upward altitudinally and northward from the late 1980s to 2008. According to latitude (or altitude), the cropland increased areas seemed to be about 0.5°-1° more northward (or 100-200 m higher) than the decreased areas (Shi and Yang, 2010). In parallel, precipitation can also influence the cropland distribution. From 2001 to 2010, with every 100 mm increase of precipitation in the driest month, the deforestation probability of Jamaica increased by 8% (Newman et al., 2014). As for the cropland in China from 2000 to 2005, the main driving force of cropland-forest transition was the months whose precipitation > 50 mm (the weight range was 2.065) (Gao and Yi, 2012).
Combined with climate change scenarios, some scholars predicted the cropland response to future climate change. As for the tropical ecosystems, humidity and extreme heat were projected to negatively impact the growing season length and the crop suitability (medium confidence) (Jones and Thornton, 2009). Lane and Jarvis (2007) used projected future climate data for ~2055 and the Ecocrop model to predict the areas suitable for 43 crops. Results indicated that suitable cropland areas are projected to grow, however, the suitable areas for the cold weather crop were likely to decrease significantly, including wheat (18%). By region, Europe was projected to increase by 3.7% in suitable cropland areas, suitable areas in Antarctica and North America would also expand by 3.2% and 2.2%, respectively, suitable cropland areas in Sub-Saharan Africa and the Caribbean were likely to experience a decline (-2.6% and -2.2%, respectively). To 2050, more than 50% of the cropland was projected to be unsuitable for cultivating in most African countries (Burke et al., 2009). With the climate projections of the global coupled atmosphere-ocean general circulation model (version 2) by the Meteorological Research Institute of the Japan Meteorological Agency, during the period 2081-2100, rice cultivation area in Japan was projected to move northward from 100 km to 200 km (Ohta and Kimura, 2007). Under the IPCC SRES A1B and A2 climate change scenarios, from 2005 to 2035, cropland area of the Poyang Lake region was projected to increase by 3% and 2.3%, respectively, the cropland area under the B1 scenario was likely to decrease by 1% (Yan et al., 2013). Compared to the period 1961-1990, with the 80% and 50% guarantee rates of accumulated temperature, planting boundaries of early and middle maturity varieties were likely to move northward 1.9°-2.3° latitude and 1.2°-2.6° latitude, respectively. For the late-maturity spring maize in Heilongjiang and Liaoning, their planting boundaries would move northward 2.0°-3.9° latitude and 0.4°-1.7° latitude, respectively (Liu et al., 2010).

3.2 Human activities

Globalization, urbanization and industrialization substantially influence the farmers’ living and land use, subsequently cause the cropland change. For example, economic development and urbanization can lead to the decrease of cropland area (Li, 1999). From 1978 to 2007, for every 1% increase of urbanization level and local finance revenue, the area of cropland abandonment increased by 0.05% and 0.03%, respectively (Huang et al., 2009). From 1997 to 2008, for every 1% increase of urbanization level of Jiangsu Province, the cropland area decreased by 1800 ha (Meng et al., 2013). Meanwhile, during 1978-2007, the cropland lost 5671.40 ha with every 1% increase of urbanization level of Chengdu (Chen et al., 2010).
Population growth promotes the demand of minerals, land and water resources, and drives the conversion of cropland to non-agriculture use (Newman et al., 2014; Zhan et al., 2010). For every 1% population increase of Jiangxi Province, the conversion of cropland to urban and industrial land raised 0.802%; for every 1% increase of the proportion of agricultural population, the conversion of cropland to forest/grassland increased by 1.131% (Zhan et al., 2010). Zhang et al. (2010) found that impacts of non-agricultural population proportion on cropland have exceeded that of total population. For every 1% increase of total population and the proportion of agricultural population, cropland areas reduced by 0.90‰ and 1.33‰, respectively.
Location and transportation are also the important driving forces of cropland distribution. There is general agreement that the probability of cropland abandonment grows with the distance to the settlement (Schweizer and Matlack, 2014). For the provinces of Kaluga, Rjazan, Smolensk, Tula and Vladimir in European Russia (for five provinces in post-Soviet European Russia), from 1990 to 2000, an additional kilometer far from settlements increased the probability of cropland abandonment by 8% (Prishchepov et al., 2013). In the Ongiud Banner of Inner Mongolia, being one additional kilometer closer to the nearest settlement increased the probability of cropland reclamation by 1.6 times (Xie and Li, 2008). Meanwhile, the distances to road and town also have negative effects on cropland abandonment. In Fuyang County of Zhejiang Province, for each 100 m increase in distance to road and town, the risk of being abandoned decreased 0.9802 and 0.9704 times, respectively (Zhong et al., 2011). Forest area impacts the cropland abandonment positively, the distance to forest impacts it negatively. For each hectare expansion of native forest in 1985, the probability of cropland abandonment increased by 0.23%. Also, for every one kilometer closer to Chiloé National Park, the probability of cropland abandonment increased by 0.45% (Díaz et al., 2011). For five provinces in post-Soviet European Russia, the probability of cropland abandonment decreased by 4% for each 100 m increase of distance to the forest edge and increased by 48% for the cropland areas within the forest matrix (Prishchepov et al., 2013). In addition, cropland transition is closely related to its neighboring land use. For Fuyang City in Zhejiang Province, an additional 100 m away from the nearest construction land decreased the probability of being converted by 0.6703 times (Zhong et al., 2011). For Jiangsu Province, during the period 1998-2008, an additional 1% decrease of adjacent cropland area led to 0.154% decrease of local region (Wen et al., 2011).
The land use (or migration) policy differences between regimes or periods impact the farmers’ attitude towards the cropland (Zhong et al., 2011; Díaz et al., 2011). In general, the existence of cropland protection policy restrains the land abandonment. In Southern Chile, the presence of a subsidy reduced the risk of cropland abandonment by 19% (Díaz et al., 2011). For the Fuyang City of Zhejiang Province, an additional one unit land protection policy decreased the probability of being converted by 1.0231 times (Zhong et al., 2011).
Technology is so important that can solve the livelihood of increasing population with the limited land. However, it is difficult to analyze quantitatively. In the long haul, technical progress may result in the cropland shrinkage (Ewert et al., 2007). During the period 1996-2011, for each increase of one unit technical factor, the area of Chinese cropland reduced by 0.003 % (Zhang and Chen, 2014).
Other driving forces are also investigated in literatures. For example, in the five provinces in post-Soviet European Russia, an additional 0.1 t/ha decrease of grain yields in the late 1980s, increased the risk of cropland abandonment between 1990 and 2000 by 11% (Prishchepov et al., 2013). Adjustment of agricultural structure also affects the cropland pattern, during the period 1998-2008, the marginal elasticity coefficient of ratio of grain and economic crops on cropland change is 0.069 in Jiangsu Province (Wen et al., 2011).
Summary of the integrated contribution of driving forces is helpful to understand the factors that are putting stress on cropland. Based on the hierarchical partitioning analysis, Prishchepov et al. (2013) found that ‘average grain yields in the late 1980s had the highest explanatory power for cropland abandonment (42.1% of the total variability), whereas ‘distance from nearest forest edge’ was of secondary importance (19.4%), it was followed by ‘isolated cropland within the forest matrix’ (11.9%) and ‘distance from nearest settlement with more than 500 people’ (11.5%), human influences were more obvious than that of climate change. Zhang et al. (2014) employed a multi-level statistical model to explore the driving forces of cropland abandonment of Wulong County, Chongqing. The research revealed that 7% and 13% of the cropland abandonment can be attributed to the household and village levels, respectively, while the remaining 80% can be attributed to the land parcel features.

3.3 Integrated contributions of climate change and human activities to cropland change

Integrated contributions of climate change and human activities to cropland change vary with regions. With regard to land use degree excursion intensity, 81% and 85% was caused by climate changes in east-west direction and north-south direction, respectively. The climate change impacts were much greater than human impacts (Gao and Liu, 2006). While for Xinjiang Autonomous Region, from 1981 to 2005, the contributions of climate change in east-west direction and north-south direction were 24% and 40%, respectively, climate change influences were less than that of the human activities (Huang et al., 2009). Furthermore, integrated contributions of climate change and human activities to cropland change vary with conversion types and periods. For example, the reversed desertification mainly caused by climate change during 1981-1990 (the contribution was 64.30%) and by human activities during 1991-2000 (the contribution was 91.13%). The expanded desertification was mainly induced by human activities between 1981 and 1990 (the contribution was 89.16%) and by climate change between 1991 and 2000 (the contribution was 79.42%) (Xu et al., 2009).
For the contributions of future climate change and human activities to the cropland pattern, the socio-economic factors (economics, technology reform, social development and governmental structure) play more important role to cropland change (Tubiello and Fischer, 2007). To 2080, without consideration of climate change, the cropland in developing countries was projected to rise by 27% (250 Mha), most of these was from Africa (+122 Mha, or +60%) and Latin America (80 Mha, or +45%). On the contrary, croplands of many developed countries were projected to decrease; Western Europe had the largest reduction (-9 Mha, or -11%). With consideration of combined effect of climate and economic changes, under IPCC SRES A2 climate scenario, impacts of climate change on global cropland were projected to be small (+9 to +12 Mha, or +0.5 to +0.7%). Anthropogenic impact was projected to be more significant than that of climate change (Tubiello and Fischer, 2007). Under the influence of human activities, cropland in England and Wales was projected to reduce from 3.48 Mha in the mid-1980s to 2.07 Mha in 2060. While under the climate scenarios from Geophysical Fluid Dynamics Laboratory, Goddard Institute for Space Studies, and United Kingdom Meteorological Office, the cropland areas were likely to be 2.23 Mha, 2.05 Mha and 2.18 Mha, respectively (Hossell et al., 1996). Briner et al. (2012) found that, for the Visp region in the Swiss Alps, the cropland loss from economic change (147 ha) was projected to be larger than that from climate change (116 ha).

4 Limitations

4.1 Method issue

The methods of identifying the contributions of climate change and human activities to cropland change have their own advantages and disadvantages. Some of these methods investigate the integrated role of climate change or human activities, and some others investigate the relative role of specific factors of climate change and human activities. The integrated role and specific role are seldom considered simultaneously. First of all, we should analyze the integrated role of climate change or human activities; then, we should know what are the particular factors inducing the cropland change. In addition, there are few methods for identifying the contributions of climate change or human activities to cropland change shown at the detailed spatial scale.

4.2 Data issue

Compared with the spatial pattern data for cropland from high resolution remote sensing image, data of driving forces are needed to be more accurate and abundant. For example, data on the discriminate of rain-fed, irrigation and wetland cultivation, crop types, cropping system, pesticide and fertilizer, etc. are not yet accurately and adequately acquired. However, these data are indeed vital important factors relating to research driving forces. Moreover, the socio-economic statistical data (economic, political and technical) are limited to display and mostly collected based on administrative district, which disagree with the cropland pattern data based on the physical characteristics (Yu et al., 2013). Except for some population data and national statistical data, the comparable data of driving forces are absent worldwide. Moreover, the existing socio-economic data are mainly from developed countries (IPCC, 2014). All above restrict the deep understanding of driving mechanism of land use changes.

4.3 Scale issue

Land use is an integrated decision of multi-scale and multi-dimension, so the contributions of climate change and human activities to cropland change are different in different spatial-temporal scales and actor scales. The same driving forces impact differently in different regions, and the cropland change could be interpreted by changes at various scales simultaneously (Napton et al., 2010). Most of the researchers can realize the time scale, but ignore the treatment of spatial scale, actor scale and classification precision. The differences of the contributions among various scales are scarcely researched, which results in the unilateral understanding of driving forces.

4.4 Explanation issue

The explanation of the relationships between cropland and driving forces is “grey”. Simultaneously, the studies seldom investigate the individual effect of climate change and human activities. Evaluating the contributions of the climate change and human activities systematically is useful to understand the mechanism of reclamation and abandonment of cropland. We should explain the results carefully because of the imperfection comprehension of impacts on cropland system (Yu et al., 2013).

5 Perspectives

5.1 Identifying the contributions from integrated role to specific factors

The evaluation of the contributions of climate change and human activities should aim at the spatial quantitative identification. Based on the image interpretation data of cropland, we should refer to the causality between driving forces and cropland pattern in the framework analysis method and also the ideas from difference comparison, and select the suitable model to project the potential distributions of cropland only under climate change. Then, we can compare the potential distributions with the actual changes of cropland, to investigate the influences of human activities, and identify the integrated contributions of climate change and human activities. In addition, combined with the changes of water and heat conditions, questionnaires should be investigated in the reclamation and abandonment areas of cropland, and the spatial choice model should be employed to explore the internal mechanism of the reclamation and abandonment (Yin et al., 2010). The contributions of climate change and human activities to cropland spatial-temporal change can be spatially identified. Moreover, the results validation should be considered in the future evaluation.

5.2 Enhancing the comprehensiveness and accuracy of data

We should pay more attention to the collection of high quality cropland data, such as the cropping system, crop types, management (irrigation, fertilization), natural disaster, etc. Meanwhile, we should supplement the socio-economic data from the questionnaire of representative regions, such as the economic level of household, labor structure, migrant laborers, livestock and villages. We should also seek the appropriate model (or method) to mate the socio-economic data with natural environment data, and display the former spatially. Moreover, based on the high-resolution remote sensing images, information of human activities should be extracted to provide the reliable support of establishment and validation of model. In addition, the traditional statistical method requires the normal distribution and linear data (Tsakovski et al., 2010). However, in the self organization theory, knowledge acquiring is self-adaption and fault-tolerant, which can explain the non-linear and macro characteristics of the open complex system. It can be used to solve the complex, multi-dimension and non-linear relationships between cropland use and climate change, human activities.

5.3 Quantitatively synthesizing with multi-scale and multi-dimension

The scales that impact on cropland use include time, spatial and actor scale. As for the time scale, what are the relationships between drivers and cropland use at monthly, annual, decadal and centenary scale respectively? As for the spatial scale, what are the relationships between drivers and cropland use at parcel, local, regional, national and global scale respectively? At the various spatial-temporal scales, what are the contributions of the actors from farmer to the institution scale? All of these questions should be answered by the multi-scale and multi-dimension researches (Cai, 2001). Cropland utilization is the decision of multi-scale action. Multi-scale statistical models would seem to solve the nested structure activity and could be taken as commendable attempt to determine the contributions at different scales (Zhang et al., 2014). First of all, the representative explanatory variables should be collected. For example, the slope, elevation and soil quality represents the variables at parcel scale, agricultural labor amount, labor age and percentage of male laborer represent the variables at household scale, distance to administrative center and land rental rate represent the variables at village scale. The models are constructed by including the explanatory variables at different scales sequentially. Then the contributions of variables at different scales can be determined by the comparison of models.

5.4 Reasonable explaining on the basis of driving force theory

We should realize that the methods are only the tools to understand the complicated relationships between cropland change and its driving forces (Gao and Yi, 2012). In order to discuss the contributions of climate change and human activities to cropland change, the explanation should appeal the co-integration test and Granger causality test analyses to judge the causality firstly. Therefore, we should deepen the comprehension of drivers and avoid analyzing the results just in terms of the simulations or statistical results.

The authors have declared that no competing interests exist.

References

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[1]
Audsley E, Pearn K R, Simota Cet al., 2006. What can scenario modelling tell us about future European scale agricultural land use, and what not? Environmental Science & Policy, 9(2): 148-162.Scenario modelling has identified several such regions where there is a need to be watchful, but few where all of the scenario results agree, suggesting great uncertainty in future projections. Thus, it has not been possible to predict any futures, though all results agree that in Central Europe, changes are likely to be relatively small.

DOI

[2]
Beilin R, Lindborg R, Stenseke Met al., 2014. Analysing how drivers of agricultural land abandonment affect biodiversity and cultural landscapes using case studies from Scandinavia, Iberia and Oceania.Land Use Policy, 36: 60-72.

[3]
Benini L, Bandini V, Marazza Det al., 2010. Assessment of land use changes through an indicator-based approach: A case study from the Lamone river basin in Northern Italy.Ecological Indicators, 10(1): 4-14.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">This study has been performed in order to evaluate the land use changes and related environmental impacts which occurred in recent decades in the Lamone river basin (Northern Italian Apennines). Using the DPSIR indicator-based approach, agricultural land use changes and conversions occurred within the periods 1976&ndash;1994 and 1994&ndash;2003, have been associated with the shortage of water in the river and the modification of the landscape structure. Results show that Lamone river basin in 1976 was mainly dominated by forest (27.4%) and cropland (32.3%) and through the entire period the valley presented a strong persistence of land uses and the main conversions detected are afforestation and agricultural intensification. The hydrological balance analysis results indicate that the agricultural intensification process produced a water deficit in summer periods equal to 0.89&#xA0;mln&#xA0;m<sup>3</sup> in 2003. The landscape of the Lamone valley became more homogeneous, showing a decrease in diversity (Shannon Diversity Index values decreased from 1.81 to 1.58) and the riparian corridor became more human-dominated (Human Habitat values increased from 0.61 to 0.77). An integrated assessment of possible management options has been conducted, using the MULINO-DSS software as a support. Thirteen different management scenarios have been produced in order to solve the water balance issue and to enhance the riparian corridor. Attributing equal weight to the environmental, social and economical criteria, the best solution corresponds to the sole creation of artificial basins and the actual situation is placed at position 8 (out of 13).</p>

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[4]
Briner S, Elkin C, Huber Ret al., 2012. Assessing the impacts of economic and climate changes on land-use in mountain regions: A spatial dynamic modeling approach. Agriculture, Ecosystems & Environment, 149: 50-63.Future land-use changes are predicted to be influenced by both climate-driven environmental changes and concomitant changes in local economic conditions. Assessing the impact of climate change on ecosystems, and the goods and services that they provide, therefore requires an understanding of the dynamic link between land-cover, ecosystem services and economic-driven land-use decisions. The economic land allocation model (ALUAM) simulates the competition between forest and a range of agricultural land-uses to estimate land-use conversions in a spatially explicit manner at high resolution. Using a modular framework, ALUAM was linked with the forest-landscape model LandClim, and a crop yield model, that simulate the response of forests and crops to changes in climate. An iterative data exchange between the models allows a detailed assessment of the dynamic changes in the provision of agricultural and forest based services. We apply our model to the temperature sensitive inner-alpine region of Visp, Switzerland. Our results demonstrate that land-use is influenced directly by environmental shifts and economic decisions, but are also highly dependent on the interactions between these two components. These shifts in land-use will correspondingly affect the provision of ecosystem goods such as food and timber production.

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[5]
Bürgi M, Hersperger A M, Schneeberger N, 2004. Driving forces of landscape change: Current and new directions.Landscape Ecology, 19(8): 857-868.The concept of driving forces is gaining increasing attention in landscape-change research. We summarize the state of the art of this field and present new conceptual and methodological directions for the study of driving forces of landscape changes. These new directions address four major challenges faced by landscape-change studies, i.e., studying processes and not merely spatial patterns, extrapolating results in space and time, linking data of different qualities, and considering culture as a driver of landscape change. The proposed research directions include: studying landscape change across borders and transects, focusing on persistence as well as change, investigating rates of change, considering attractors of landscape change, targeting correlation and causality, and searching for precursors of landscape change. Based on established knowledge and the new approaches we outline a standard procedure to study driving forces of landscape change. We anticipate that our analytical and systematic approach increases the relevance of studies of landscape change for science as well as for the solution of real world problems.

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[6]
Burke M B, Lobell D B, Guarino L, 2009. Shifts in African crop climates by 2050, and the implications for crop improvement and genetic resources conservation.Global Environmental Change, 19(3): 317-325.Increased understanding of the substantial threat climate change poses to agriculture has not been met with a similarly improved understanding of how best to respond. Here we examine likely shifts in crop climates in Sub-Saharan Africa under climate change to 2050, and explore the implications for agricultural adaptation, with particular focus on identifying priorities in crop breeding and the conservation of crop genetic resources. We find that for three of Africa's primary cereal crops – maize, millet, and sorghum – expected changes in growing season temperature are considerable and dwarf changes projected for precipitation, with the warmest recent temperatures on average cooler than almost 9 out of 10 expected observations by 2050. For the “novel” crop climates currently unrepresented in each country but likely extant there in 2050, we identify current analogs across the continent. The majority of African countries will have novel climates over at least half of their current crop area by 2050. Of these countries, 75% will have novel climates with analogs in the current climate of at least five other countries, suggesting that international movement of germplasm will be necessary for adaptation. A more troubling set of countries – largely the hotter Sahelian countries – will have climates with few analogs for any crop. Finally, we identify countries, such as Sudan, Cameroon, and Nigeria, whose current crop areas are analogs to many future climates but that are poorly represented in major genebanks – promising locations in which to focus future genetic resource conservation efforts.

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[7]
Cai Y L, 2001. A study on land use/cover change: The need for a new integrated approach.Geographical Research, 20(6): 645-652. (in Chinese)Land cover changes are not simple processes. There are complex simultaneous patterns of land-cover change, ranging from modifications in land cover to conversions and maintenance. There is a functional complexity within types of land-cover change, and a structural complexity between types of land-cover change, both in terms of spatial arrangements and temporal patterns of change. Land-cover change needs to be measured in its complexity to fully understand it. It is important to differentiate between land cover and land use when measuring patterns of changes. Land-use/land-cover change is a kind of extremely complex phenomenon. For all researchers involving in the study to avoid the unilateralism like blind-person touching elephant, they should search a new synthesis of studies. Therefore, the traditional approach of land use study is not effective and new topics are needed to be further found. A generalized and comprehensive understanding is required for the drivers of land-use change. We need a network of case studies that represents the spatial heterogeneity of the region and a multi-level approach that allows for a linkage between regional and local scale land-cover dynamics. Case study comparison is a major tool to derive generalizations of land-use/land-cover change research. We should develop new methods in mathematical modeling, descriptive models, empirical study, systematic case study and mechanism study. Linking house-hold-level information to remote sensing data is becoming a major tool to increase our understanding of land-use dynamics. The drivers of LUCC are always present but interact differently according to the temporal and spatial dynamics of the situation. A thorough understanding and modeling of these complex interactions is a prerequisite to generate realistic projections of land-cover change. The more important hypotheses for LUCC are those that frame the integration and synthesis of the science.

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[8]
Chen C Q, Qian C R, Deng A Xet al., 2012. Progressive and active adaptations of cropping system to climate change in Northeast China.European Journal of Agronomy, 38: 94-103.To learn the historical response of cropping system to climate change will benefit the strategy decision of future cropping adaptation. In this paper, we conducted an integrated analysis of the climate records of seventy-two meteorological stations and the records of crop yields over the period 1970-2009 in Northeast China. It was found that over these forty years, the daily mean, maximum and minimum temperatures during crop growing season increased on average by 0.34°C, 0.28°C, 0.43 °C every ten years, respectively. No significant change in the precipitation was found, although the differences between years were large. After de-trending the agronomic technique contributions to the increments of crop yields, the historical warming had led to great annually increments of 16.6 kg ha

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[9]
Chen H J, Deng L J, Li H Cet al., 2010. Research on coordination between the urbanization development and cultivated land change in Chengdu City.Chinese Agricultural Science Bulletin, 26(1): 312-316. (in Chinese)China’s urbanization process has entered a accelerated development stage,the scale of the urban construction site has extended,which was an important cause of the cultivated land has decreased.Author analyzed the changes of the urbanization level and the cultivated land resources,studied the Correlation and coordinated index between the urbanization level and the cultivated land resources of Chengdu city from 1978 to 2007 based on statistical data by methods of regression analysis and coordination analysis.The result showed that the level of urbanization was rising,the total area of cultivated land and per capita arable cultivated land was decreasing year by year;there was reverse correlation between the urbanization level and the cultivated land area,the Coordinated index between the urbanization level and the cultivated land area was a nonlinear relation,the overall trend was" concessive and basically concessive→not concessive→concessive and basically concessive"in the past 30 years in Chengdu City.

[10]
Corbelle R E, Crecente M R, Santé R I, 2012. Multi-scale assessment and spatial modelling of agricultural land abandonment in a European peripheral region: Galicia (Spain), 1956-2004.Land Use Policy, 29(3): 493-501.ABSTRACT The environmental, cultural and economic consequences of land use change, including abandonment of agricultural use, have been recognized for a long time. It has often been assumed that the transformations of the agricultural systems in developed countries (and particularly in Europe) took place, for the main part, in the immediate years after the Second World War. In this paper we present a review of different statistical and cartographic sources available for a peripheral region in Europe (Galicia, Spain) characterized by small-scale farming and a very fragmented property system, that suggests otherwise: modernization of agriculture apparently took place without major changes in agricultural area until the country gained access to the European Economic Community, and the effect of Common Agricultural Policy reforms during the decade of 1990 is suggested as a major driver for the net decrease of agricultural area in the region between 1956 and 2004. On the other hand, this paper emphasizes the spatial complexity of agricultural abandonment with a case study, showing a large degree of variability at municipal scale and thus the need of future EU-level projects to work at least at municipal (Local Administrative Units 2, LAU 2) level. Finally, a multinomial logistic model for observed changes is included that shows how biophysical limitations were the main drivers behind abandonment of agriculture at parcel scale, while structural qualities related to property structure were strongly associated to the incorporation of former shrublands to agricultural use. Besides, Farm Structure Surveys were revealed as an unreliable source for the assessment of changes in total agricultural area in the studied region.

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[11]
Díaz G I, Nahuelhual L, Echeverría Cet al., 2011. Drivers of land abandonment in Southern Chile and implications for landscape planning.Landscape and Urban Planning, 99(3): 207-217.

[12]
Dong J W, Liu J Y, Tao F Let al., 2009. Spatio-temporal changes in annual accumulated temperature in China and the effects on cropping systems, 1980s to 2000.Climate Research, 40(1): 37-48.Change in thermal conditions can substantially affect crop growth, cropping systems, agricultural production and land use. In the present study, we used annual accumulated temperatures > 10 degrees C (AAT10) as an indicator to investigate the spatio-temporal changes in thermal conditions across China from the late 1980s to 2000, with a spatial resolution of 1 x 1 km. We also investigated the effects of the spatio-temporal changes on cultivated land use and cropping systems. We found that AAT10 has increased on a national scale since the late 1980s, Particularly, 3.16 x 10(5) km(2) of land moved from the spring wheat zone (AAT10: 1600 to 3400 degrees C) to the winter wheat zone (AAT10: 3400 to 4500 degrees C). Changes in thermal conditions had large influences on cultivated land area and cropping systems. The areas of cultivated land have increased in regions with increasing AAT10, and the cropping rotation index has increased since the late 1980s. Single cropping was replaced by 3 crops in 2 years in many regions, and areas of winter wheat cultivation were shifted northward in some areas, such as in the eastern Inner Mongolia Autonomous Region and in western Liaoning and Jilin Provinces.

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[13]
Du X D, Jin X B, Yang X Let al., 2014. Spatial pattern of land use change and its driving force in Jiangsu Province.International Journal of Environmental Research and Public Health, 11: 3215-3232.Scientific interpretation of the mechanism of land use change is important for government planning and management activities. This study analyzes the land use change in Jiangsu Province using three land use maps of 2000, 2005 and 2008. The study results show that there was a significant change in land use. The change was mainly characterized by a continuous built-up land expansion primarily at the expense of cropland loss, and the trend became increasingly rapid. There was an obvious regional difference, as most of the cropland loss or built-up land expansion took place in southern Jiangsu, where the rate of built-up land expansion was faster than in central and northern Jiangsu. Meanwhile, the spatial pattern changed remarkably; in general, the number of patches (NumP) showed a declining trend, and the mean patch size (MPS) and patch size standard deviation (PSSD) displayed increase trends. Furthermore, the relative importance of selected driven factors was identified by principal component analysis (PCA) and general linear model (GLM). The results showed that not only the relative importance of a specific driving factor may vary, but the driven factors may as well. The most important driven factor changed from urban population (UP), secondary gross domestic product (SGDP) and gross domestic product (GDP) during 2000-2005 to resident population (RP), population density (POD) and UP during 2005-2008, and the deviance explained (DE) decreased from 91.60% to 81.04%. Policies also had significant impacts on land use change, which can be divided into direct and indirect impacts. Development policies usually had indirect impacts, particularly economic development policies, which promote the economic development to cause land use change, while land management policies had direct impacts. We suggest that the government should think comprehensively and cautiously when proposing a new development strategy or plan.

DOI PMID

[14]
Ewert F, Porter J R, Rounsevell M, 2007. Crop models, CO2, and climate change.Science, 315(5811): 459-460.Comment on Science. 2006 Jun 30;312(5782):1918-21.

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[15]
Gao Z Q, Liu J Y, 2006. The LUCC responses to climatic changes in China from 1980 to 2000.Acta Geographica Sinica, 61(8): 865-872. (in Chinese)lt;p>Adopted with Holdridge Life Zone Model (HLZM), Weight Centre Model (WCM) and Land Use Degree Model (LUDM), climate data of China in recent 20 years and 2-period LUCC data covering China are used to analyze the impact degree and direction of changes caused by climatic changes and human activities to China vegetation covers and land use. In recent 20 years, the rise in temperature and increase in precipitation in most parts of China have influenced not only China's biome, but also growth conditions of Holdridge life zone deeply. In this period, variations in both precipitation and temperature in Northeast China, North China and the Inner Mongolia Plateau have improved living environment and led to the transformation of Nature Covered Ecological Type from unutilized land to grassland and shrubland types, grassland and shrubland types transformed to forest and arable land. Meanwhile, China's economic development in recent 20 years, as well as land use increment in rural and urban areas for construction and transportation purposes in eastern coastal zones have made Land Use Type developed from farmland to construction land, leading to increase in land use degree index. Thereby the dual impacts by climatic changes and economic development resulted in a shift of Land Use Degree Weight Centre northeastward by 54 km. With regard to Land Use Degree Excursion Intensity, in east-west direction, 81% is caused by climatic changes and 19% by anthropogenic impacts; while in north-south direction, 85% is caused by climatic changes and 15% by anthropogenic impacts.</p>

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[16]
Gao Z Q, Yi W, 2012. Land use change in China and analysis of its driving forces using CLUE-S and Dinamica EGO model.Transactions of the Chinese Society of Agricultural Engineering, 28(16): 208-216. (in Chinese)In order to analyze the driving mechanism and to predict land use change of China in the future,CLUE-S(the conversion of land use and its effects at small regional extent) and Dinamica EGO(environment for geoprocessing objects) model were used to simulate land use change in China from 2000 to 2020 based on the land use data in 2000 and 2005 from Data Center for Resources and Environmental Sciences Chinese Academy of Sciences(RESDC).With Logistic regression and Bayesian estimation,land use suitability and spatial characters of driving factors of land use change from 2000 to 2005 in China were analyzed.The simulation results in 2005 indicated that,the predictions of LUCC(land use change in China) with CLUE-S and Dinamica EGO matched broadly with actual situation and CLUE-S was better than Dinamica EGO model in overall accuracy.However,the Markov process in Dinamica EGO could precisely predict the amount of land use change and the spatial pattern was consistent with empirical result.The simulation results of land use in 2020 showed that areas of farmland,forest,water and construction land would increase,while grassland would decrease largely.Unused land would increase with CLUE-S model but decrease with Dinamica EGO model.This article serves as the scientific foundation for land resource plan and farmland protection policy in China.

[17]
Gollnow F, Lakes T, 2014. Policy change, land use, and agriculture: The case of soy production and cattle ranching in Brazil, 2001-2012.Applied Geography, 55: 203-211.The Brazilian Amazon has experienced one of the world's highest deforestation rates in the last decades. Cattle ranching and soy expansion constitute the major drivers of deforestation, both through direct conversion and indirectly by land use displacement. However, deforestation rates decreased significantly after the implementation of the action plan to prevent and control deforestation in 2004. The aim of this study is to quantify the contribution of cattle and soy production with deforestation before and after the implementation of the action plan in the two states Mato Grosso and Par谩 along the BR-163. Specifically, we aim to empirically test for land use displacement processes from soy expansion in Mato Grosso to the deforestation frontier between 2001 and 2012. First, we calculated the relationships between deforestation rate and the change in cattle head and planted soy area respectively for the BR-163 region. Second, we estimated different panel regression models to test the association between processes of land use displacement. Our results indicate a close linkage between cattle ranching and deforestation along the BR-163 between 2001 and 2004. Soy expansion in Mato Grosso was significantly associated with deforestation during this period. However, these relations have diminished after the implementation of the action plan to control and prevent deforestation. With the decrease in deforestation rates in 2005, cattle ranching and deforestation were not directly linked, nor was soy expansion in Mato Grosso and deforestation at the forest frontier. Our analysis hence suggests that there was a close coupling of processes and spatial displacement until 2004 and a decoupling has taken place following the political interventions. These findings improve the understanding of land use displacement processes in Brazil and the methods offer potential for exploring similar processes in different regions of the world.

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[18]
Hao H G, Li X B, Tian Y J, 2010. Farmland use right transfer and its driving factors in agro-pastoral interlaced region.Transactions of the Chinese Society of Agricultural Engineering, 26(8): 302-307. (in Chinese)

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[19]
Herrmann S M, Anyamba A, Tucker C J, 2005. Recent trends in vegetation dynamics in the African Sahel and their relationship to climate.Global Environmental Change,15(4): 394-404.Contrary to assertions of widespread irreversible desertification in the African Sahel, a recent increase in seasonal greenness over large areas of the Sahel has been observed, which has been interpreted as a recovery from the great Sahelian droughts. This research investigates temporal and spatial patterns of vegetation greenness and rainfall variability in the African Sahel and their interrelationships based on analyses of Normalized Difference Vegetation Index (NDVI) time series for the period 1982鈥2003 and gridded satellite rainfall estimates. While rainfall emerges as the dominant causative factor for the increase in vegetation greenness, there is evidence of another causative factor, hypothetically a human-induced change superimposed on the climate trend.

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[20]
Hersperger A M, Bürgi M, 2009. Going beyond landscape change description: Quantifying the importance of driving forces of landscape change in a Central Europe case study.Land Use Policy, 26(3): 640-648.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Over the past decades, urban sprawl and agricultural intensification have enormously changed the traditional cultural landscape of the Swiss lowlands. This research aims to analyze the driving forces of urbanization, agricultural intensification, and greening in five municipalities of the periurban Limmat Valley, near Zurich, Switzerland. The main objectives of the paper are (1) to quantify the change in urbanization, agricultural intensification, and greening, (2) to determine the driving forces of landscape change, (3) to determine the relative importance of socioeconomic, political, cultural, technological, and natural/spatial driving forces, and (4) to establish from which administrative levels and spatial scales the most important driving forces originate. Changes for the periods 1930&ndash;1956, 1957&ndash;1976, and 1977&ndash;2000 are documented based on a comparison of cartographic maps. A list of 73 potentially relevant driving forces is established based on document analysis. Based on further document analysis and expert interviews, 52 of them were found to be relevant primary driving forces for the documented landscape changes. We found that in all three periods, urbanization was the most important process of change. Greening is steadily increasing in importance and surpassed agricultural intensification in the last period. Overall, as well as for urbanization, the economic driving forces, followed by political driving forces, are most important for landscape changes in all three periods. Cantonal driving forces are most important, followed by the national, local and international driving forces. By presenting an approach to quantify the contribution of major driving forces groups to landscape change this study contributes to method development in land change research.</p>

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[21]
Hossell J E, Jones P J, Marsh J Set al., 1996. The likely effects of climate change on agricultural land use in England and Wales.Geoforum, 27(2): 149-157.This article summarises the results of a modelling study that examines how the geographical pattern of agricultural land use and production in England and Wales might be affected by climate change. Various scenarios of regional climate change are considered by the model within a price and demand framework of a world food market also affected by global warming. The study concludes that over 3M ha of current farmland may become unprofitable for agriculture by 2060 (assuming no climate change). In addition, under the global warming scenarios postulated, a radical shift in the location of agricultural production, particularly of cereals, would be likely to occur. The merits of this modelling approach and its usefulness are also discussed.

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[22]
Huang Y, Bao A M, Wang A Het al., 2009. The LUCC responses to climatic change and human activity in Xinjiang in recent 25 years.Journal of Arid Land Resources and Environment, 23(10): 116-122. (in Chinese)Applied with Holdridge Life Modle,Land Use Drgree Model and Weight Centre Model,climate data of Xinjiang in recent 25 years and 2-period LUCC data were used to analyze the impact degree and direction of changes caused by climate changes and human activities to Xinjiang land covers and land use.The results showed that the rise in temperature and precipitation in most parts of Xinjiang caused the change of the vegetation community pattern.Meanwhile,the increasing of human acvitivities made the farmland and construction land increasing,and the eco-environment being improved in the northwest.Thereby the dual impacts by climatic changes and economic development resulted in a shift of Land Use Degree Weight Centre northwest ward by 3.87km.With regard to Land Use Degree Excursion Intensity,in north-south direction,40% was of that caused by climatic changes and 60% of that by anthropogenic impacts;while in east-west direction,24% was of that caused by climatic changes and 76% by anthropogenic impacts.

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[23]
Huang Z H, Wu C F, Du X J, 2009. Empirical study of cultivated land change and socio-economic factors in China.Journal of Natural Resources, 24(2): 192-199. (in Chinese)With rapid socio-economic development,China's cultivated land decreased from 1.32脳108 ha in 1978 to 1.22脳108 ha in 2007.Socio-economic factors are supposed to be the main factors influencing cultivated land change.The purpose of this study is to explore the impacts of socio-economic factors including economic growth,urbanization and economic institutional factor(using local finance revenue as a proxy variable) on cultivated land.Methods of Granger causality test and error correction model were employed.Based on yearly time series of cultivated land area,GDP,urbanization level and local finance revenue from 1978 to 2007,this paper reveals the cointegration and causality relationship between cultivated land,economic growth,urbanization and local finance revenue.It also reveals the long-term and short-term effects of economic growth,urbanization and local finance revenue on cultivated land.The Granger test result shows economic growth,urbanization,and local finance revenue could cause cultivated land change.Thus it indicates that the recent cultivated land decrease is caused by rapid economic growth,urbanization and local government's pursue of maximization of local finance revenue.However,cultivated land change does not cause socio-economic development,which may indicate that China land use efficiency is not high.It is found that the finance and tax system reform in 1994 has significant negative effect on cultivated land.The impact of local finance revenue on cultivated land after 1994 is greater than that of before 1994.The error correction model results show that urbanization and local finance revenue have influence on cultivated land in the long run,while economic growth mainly affects cultivated land in the short run.The long-term cointegration equation reveals the urbanization elasticity of cultivated land is-0.05,which means that once urbanization rate increases by 1%,the cultivated land would decrease by 0.05%.The long-term cointegration equation also reveals local finance revenue elasticity of cultivated land is-0.03.The short-term error correction equation reveals the GDP elasticity of cultivated land is-0.04,while the local finance revenue elasticity of cultivated land is-0.01.The adjusting coefficient of error correction term is-0.41 which means that the velocity of short-term variation approaching the long-term equilibrium is 41%.The conclusions are: to harmonize cultivated land and socio-economic development,long-term and short-term measures should be put forward;in the long run,measures of decreasing the dependence of urbanization and local finance revenue on cultivated land should be carried out;in the short run,measures of improving land use efficiency,supervising cultivated land requisition and using cultivated land to ensure socio-economic development as less as possible should be taken.

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[24]
IPCC. Climate change 2014: Impacts, adaptation,vulnerability. Part A: Global and sectoral aspects. In: Field C B, Barros V R, Dokken D Jet al. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.Taking the example of pork,using 2(safety certification)脳2(brand awareness) situational experinment and ANOVA method studied restraining factors and mechanism of safety certification on consumer's purchase intention.Consumer's economic rationality was the precondition to trigger safety certification and stimulate consumer response.The empirical analysis found that safety certification had a significant influence on consumer purchase intention,and consumer's purchase intention was affected by the interaction of consumer's trust and brand awareness.

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[25]
Jones P G, Thornton P K, 2009. Croppers to livestock keepers: Livelihood transitions to 2050 in Africa due to climate change.Environmental Science & Policy, 12(4): 427-437.The impacts of climate change are expected to be generally detrimental for agriculture in many parts of Africa. Overall, warming and drying may reduce crop yields by 10-20% to 2050, but there are places where losses are likely to be much more severe. Increasing frequencies of heat stress, drought and flooding events will result in yet further deleterious effects on crop and livestock productivi...

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[26]
Kelarestaghi A, Jeloudar Z J, 2011. Land use/cover change and driving force analyses in parts of Northern Iran using RS and GIS techniques.Arabian Journal of Geosciences, 4(3/4): 401-411.To accomplish integrated watershed management and land use planning, it is necessary to study the dynamic spatial pattern of land use and cover change related to socioeconomical and physical parameter

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[27]
Lambin E F, Meyfroidt P, 2010. Land use transition: Socio-ecological feedback versus socio-economic change.Land Use Policy, 27(2): 108-118.lt;h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">The concept of land use transition highlights that land use change is non-linear and is associated with other societal and biophysical system changes. A transition in land use is not a fixed pattern, nor is it deterministic. Land use transitions can be caused by negative socio-ecological feedbacks that arise from a depletion of key resources or from socio-economic change and innovation that take place rather independently from the ecological system. Here, we explore whether the sources of land use transitions are mostly endogenous socio-ecological forces or exogenous socio-economic factors. We first review a few generic pathways of forest transition as identified in national case studies, and evaluate the varying ecological quality of expanding forests associated with these pathways. We then discuss possible explanatory frameworks of land use transitions. We use the case of the recent forest transition in Vietnam as an illustration. Socio-ecological feedbacks seem to better explain a slowing down of deforestation and stabilization of forest cover, while exogenous socio-economic factors better account for reforestation. We conclude by discussing the prospects of accelerating land use transitions in tropical forest countries.</p>

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[28]
Lane A, Jarvis A, 2007. Changes in climate will modify the geography of crop suitability: Agricultural biodiversity can help with adaptation.SAT eJournal, 4(1): 1-12.Climate change will cause shifts in areas suitable for cultivation of a wide range of crops. We used current and projected future climate data for ~2055, and the Ecocrop model to predict the impact of climate change on areas suitable for all crops listed in Table 1 of the International Treaty on Plant Genetic Resources for Food and Agriculture and other major staple and cash crops. Most detrimentally affected in terms of reduction of suitable areas for a range of crops will be sub-Saharan Africa and the Caribbean, areas with the least capacity to cope. Conversely, Europe and North America will see an increase in area suitable for cultivation. These regions have the greatest capacity to manage climate change impacts. To minimize the impacts of these climate and other environmental changes, it will be crucial to breed new varieties for improved resistance to abiotic and biotic stresses is. Plant breeders need to increase their attention to breeding varieties that have greater tolerance to local abiotic stresses such as drought, flooding and extreme temperatures as well as continuing to breed for resistance to pests and diseases. Priorities for breeding should consider the magnitude of the predicted impacts on productivity of the crop, the number of people who depend on the crop and their level of poverty, and the opportunities for significant gains through breeding. Local knowledge of ecological interactions, traditional varieties, and the genetic diversity in the wild relatives of domesticated crops provide rich resources on which to build priority breeding programmes for climate change-tolerant varieties.

[29]
Li X B, 1999. Change of arable land area in China during the past 20 years and its policy implications.Journal of Natural Resources, 14(4): 329-333. (in Chinese)Dynamics of arable land and its driving forces in China during the past 20 years are discussed in this paper based on statistical and survey data at national,provincial and county levels.It was found that (a)the general trend of net arable land loss started in the late 1950s which got continued and accelerated in the study period;(b)peaks of arable land loss were usually correspondent to the booming up periods of the economy;(c)the lost arable land was mostly those high quality land in the eastern part while the acclaimed land was mostly marginal land in the western part of the country;(d)area of arable land occupied by non agricultural sectors was sensitive to investment in fixed assets,indicating a low efficiency of land utilization in non agricultural sectors;and (e)urbanization rate had a positive influence on the efficiency of land utilization.The author claims that the following points should be emphasized in land management in order to protect the valuable arable land resources in the country:(a)importance should be placed on both the quantity and quality of arable land.In this connection,the present policy of 鈥渒eeping quantity balance" within an administration territory is no good to the very land required protection,namely,the high quality arable land;(b)policies on rural urban migration and the development of rural industry should be reviewed,since land utilization of the non agricultural sectors in rural areas is far more inefficient than those in urban areas; and (c)land market construction in both urban and rural areas should be strengthened,since the poor circulation of land resources is a major factor of the low land utilization efficiency in China.

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[30]
Liu J Y, Zhang Z X, Xu X Let al., 2009. Spatial patterns and driving forces of land use change in China in the early 21st century.Acta Geographica Sinica, 64(12): 1411-1420.(in Chinese)lt;p>Land use and land cover change as the core of coupled human-environment systems has become a potential field of land change science (LCS) in the study of global environment change. Based on remotely sensed data of land use change with the spatial resolution of 1km &times;1km on national scale among every five years, this paper designed a new dynamic regionalization according to the comprehensive characteristics of land use including regional differentiation, physical, economic, and macro-policy factors as well. Spatial pattern of land use change and its driving forces were investigated in Chia in the early 21st century. To sum up, land use pattern of this period was characterized by rapid changes in the whole country. Over the agricultural zones,e.g., Huang-Huai-Hai Plains, the southeast coastal area and Sichuan Basin, the built-up and residential areas were considerably expanded to a great proportion in the northwestern oasis agricultural zones and the northeastern zone led to a slight increase of arable land aea in the northern China. Due to the &quot;grain for green&quot; policy, forest area was significantly increased in the middle and western developing region,Where the vegetation coverage was substanially enlarged, likewise. This paper argued the main driving forces as the implementation of the strategy on land use and regional development, such as the &quot;Western Development&quot; &quot;the Revitalization of the Northeast&quot; policy, coupled with rapidly economic development during this period.</p>

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[31]
Liu Q, Chen L G, Yang Jet al., 2010. Econometric analysis on driving forces of cultivated land quantity change in Changsha-Zhuzhou-Xiangtan urban agglomerations.Resource Science, 32(9): 1734-1740. (in Chinese)Cultivated land is the core of land and plays a fundamental role in ensuring food security.With rapid social and economic development in China,cultivated land change has become increasingly marked.A great deal of modeling work has been done to detect driving forces of cultivated land change.Changsha-Zhuzhou-Xiangtan urban agglomerations are a typical zone exhibiting significant decreases in cultivated land in Hunan Province.Due to the agricultural structure adjustment,"Grain-for-Green Project",and occupation of cultivated land,Changsha-Zhuzhou-Xiangtan urban agglomerations have experienced dramatic reductions in the area of cultivated land,showing an increasingly aggravated contradiction between cultivated land supply and demand.To understand the characteristics of the cultivated land change and its driving forces,the authors performed a study on Changsha-Zhuzhou-Xiangtan urban agglomerations characterized by rapid urbanization.In this work,temporal trends in cultivated land change were systematically analyzed on the basis of statistical and survey data regarding land use in the study area.Primary contributors for cultivated land change were examined by the principal component analysis(PCA)and multiple regressive analysis.The internal relationships between the cultivated land quantity and the major driving forces were verified by the co-integration test and Granger causality test analyses.Results show that the cultivated land quantity in Changsha-Zhuzhou-Xiangtan urban agglomerations has decreased during recent 15 years,which is probably due to the rapid socio-economic development and some public policies.It was also found that there don't exist the co-integration relationships between the cultivated land change and the increase in forest land area resulting from the policy on Sloping Land Conversion(to forests) Program(SLCP).Therefore,it is difficult to carry out the Granger causality test in this case;and the argument that per capita net income of rural residents shows effects on significant changes in cultivated land quantity cannot be legitimately expressed by the Granger causality test analysis.The findings of this study would provide scientific basis for reasonable use and protection of cultivated land,slowing down the decrease rate of cultivated land as well as sustainable agricultural development in the Changsha-Zhuzhou-Xiangtan urban agglomerations.It is suggested that improving economy and the quality of life cannot lead to decreases in cultivated land directly.Strictly executing the Farmland Protection Act is the key point.Furthermore,enhancing study of farmland change would be conducive to reasonable utilization and protection of cultivated land resources,inhibiting shrinking in the area of cultivated land,maintaining the dynamic equilibrium between quality and quantity of cultivated land,and ultimately promoting regional sustainable agricultural development.

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[32]
Liu Z J, Yang X G, Wang W Fet al., 2010. The possible effects of global warming on cropping systems in China (IV): The possible impact of future climatic warming on the northern limits of spring maize in three provinces of Northeast China.Scientia Agricultura Sinica, 43(11): 2280-2291. (in Chinese)<FONT face=Verdana>【Objective】 The fact that temperature in China had increased significantly in the context of global climate change has become a consensus. The effects of this change on China’s agricultural production, especially on cropping systems and crop distribution have attracted more and more attention of the Chinese government and scientists. 【Method】 In this paper, using the accumulated temperature with different guarantee rates by empirical frequencies method, the changes of the northern limits of different maturity-types of spring maize varieties and the changes of water deficient ratio in the study region were analysed, based on the meteorology data in both A2 and B1 climatic scenarios. 【Result】 The northern limits of different maturity-types of spring maize varieties moved northward with different degrees without considering the effect of elevated CO2 concentration on the growth and development of crop in future climate scenarios. In the sensitive region, the early-maturity varieties will be replaced by the middle and late maturity varieties, making the growth period longer and the dry matter increased, which can improve the yield of spring maize in three provinces of Northeast China. However, the increase of water deficient ratio will bring a certain risk of northward movement in the 21 mid-century. 【Conclusion】 Heat and water resources should be comprehensively considered in the northward movement of different maturity-types of spring maize varieties. Spring maize should plant in the suitable region in order to reduce the loss of maize yield due to the draught. <BR></FONT>

[33]
Meng P, Hao J M, Zhou Net al., 2013. Difference analysis of effect of rapid urbanization on cultivated land changes in Huang-Huai-Hai Plain.Transactions of the Chinese Society of Agricultural Engineering, 29(22): 1-10. (in Chinese)Through the research of the internal relations of urbanization process and cultivated land change in Huang-Huai-Hai Plain, the paper reveals the regularity of urbanization process in different regions. The purpose is to provide a decision basis for the preservation of cultivated land and the sustainable development of urbanization in China based on data analysis and empirical contrast. By using the method of principal component analysis(PCA) and multiple linear regression model, land use change, the quality and quantity changes of cultivated land in Huang-Huai-Hai plain were analyzed. There were obvious differences in the 5 provinces and 2 cities during the urbanization from 1997 to 2008, such as the urbanization process, the speed of economic growth, the changes of industrial structure, growth of urban land area. With the rapid urbanization, the area of cultivated land presented decreasing trend year by year. The enhancing range and the development speed of urbanization negatively related with the change intensity and reducing speed of cultivated land change, and the change also presented a wave of increase and decrease. The cultivated land change of study area showed the obvious regional characteristics. For example, the biggest decline of cultivated land area in Hebei Province reached 210,100 hm2, while the least decline of cultivated land area in Jiangsu Province reached 26,400 hm2. The overall quality of cultivated land presented decline trend. More than 70% percent of the occupation of the cultivated land for the urban construction was the high quality arable land with good location, irrigation facilities, highly production capacity. However, the quality of arable land newly increased by reclamation and new development was lower. By selecting population urbanization factors, economic urbanization factors, spatial urbanization factors and lifestyle urbanization factors, the index system of driving force of urbanization was established. Based on PCA, some regularities can be revealed: At first, the population growth affecting on cultivated land change in these regions was the most direct and common; Secondly, the promoting functions of economic indicators were significant different due to the features of urbanization and the level of urbanization. Next, the influence of construction land growth on cultivated land was very significant, because that the space urbanization indicators load was higher; Lastly, life urbanization indicators were positive and high load, which showed that the attractions of lifestyle including income differences have an obvious role in promoting urbanization. Based the multiple linear regression model of driving force factors of urbanization and cultivated land change, it was showed that the commonness and difference of the relationship between urbanization process and cultivated land change in different regions in Huang-Huai-Hai Plain. According to those analyses, some conclusions and suggestions can be put forward. The urbanization can be promoted timely and moderately, to avoid massively occupying arable land at the expense of food security and ecological environment. So the conclusion can be drawn that occupation of less farmland, intensive use of construction land and preservation of cultivated land is the wise choice for the new urbanization development model with reasonable environmental policies.

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[34]
Müller D, Leitão P J, Sikor T, 2013. Comparing the determinants of cropland abandonment in Albania and Romania using boosted regression trees.Agricultural Systems, 117: 66-77.The collapse of socialist governance structures in Central and Eastern Europe led to the widespread abandonment of agricultural land. We estimated and compared the determinants of cropland abandonment in Albania and Romania during the postsocialist transitional period from 1990 to 2005. The data set included cropland abandonment derived from satellite image analysis, spatially continuous biogeophysical indicators, and socioeconomic surveys. Data were analyzed using boosted regression trees. Boosted regression trees can account for nonlinearities and interactions between variables and combine high predictive accuracy with appealing options to interpret the results. The results revealed important similarities between cropland abandonment in the countries and showed a strong correlation of abandonment with elevation and slope. Differences between cropland abandonment in Albania and Romania were apparent when the influence of topography was excluded. While physical accessibility tended to be more important in Albania, the density of cropland and input intensity were more decisive in Romania. The immediate time period following the collapse of socialism was dominated by extensive cropland abandonment in areas where agricultural production was no longer profitable. Gradual changes were observed in later stages of the transition period.

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[35]
Napton D E, Auch R F, Headley Ret al., 2010. Land changes and their driving forces in the Southeastern United States.Regional Environmental Change, 10(1): 37-53.The ecoregions of the Middle Atlantic Coastal Plain, Southeastern Plains, Piedmont, and Blue Ridge provide a continuum of land cover from the Atlantic Ocean to the highest mountains in the East. From 1973 to 2000, each ecoregion had a unique mosaic of land covers and land cover changes. The forests of the Blue Ridge Mountains provided amenity lands. The Piedmont forested area declined, while the developed area increased. The Southeastern Plains became a commercial forest region, and most agricultural lands that changed became forested. Forests in the Middle Atlantic Coastal Plain declined, and development related to recreation and retirement increased. The most important drivers of land conversion were associated with commercial forestry, competition between forest and agriculture, and economic and population growth. These and other drivers were modified by each ecoregion鈥檚 unique suitability and land use legacies with the result that the same drivers often produced different land changes in different ecoregions.

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[36]
Newman M E, McLaren K P, Wilson B S, 2014. Long-term socio-economic and spatial pattern drivers of land cover change in a Caribbean tropical moist forest, the Cockpit Country, Jamaica. Agriculture,Ecosystems & Environment, 186: 185-200.Very little research has considered the underlying drivers of land cover change in Caribbean islands, particularly in those islands that are still experiencing a net loss of forest cover. We investigated the underlying driving forces (socio-economic drivers) and spatial pattern drivers (biophysical features) of both deforestation and reforestation in the Cockpit Country, Jamaica. This area is one of the most globally important sites for plant diversity, but is threatened by clearance for small-scale agriculture. Drivers of change were assessed for both the individual time steps within the study period (1942鈥2010) and for the entire 68 years using multivariate, spatially explicit, statistical models. The primary drivers of deforestation over the study period were accessibility (gentler slopes, closer to forest edges, more fragmented forests) and greater relative wealth/socio-economic status (increased access to piped water). Reforestation generally increased closer to forest edges and in areas with lower market access (greater distances to roads and towns) and lower wealth/status (increased reliance on pit latrines). We found considerable temporal variation among the most important drivers for each time step, including climate, employment status, population density, population age structure and relative wealth. Forest reserve status was not a key determinant of deforestation but did increase the probability of reforestation between 1961 and 1980. During the final time step (2001鈥2010) access was less important as a deterrent to deforestation, which increased within the most contiguous forest blocks. If the deforestation drivers of the last decade do not change, deforestation is predicted to occur within the forest reserves, and in the largest, least fragmented forest blocks. Thus, conservation and management strategies for our study site must seek to address issues related to both enforcement and the socio-economic factors that influence deforestation and habitat fragmentation.

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[37]
Ohta S, Kimura A, 2007. Impacts of climate changes on the temperature of paddy waters and suitable land for rice cultivation in Japan.Agricultural and Forest Meteorology, 147(3/4): 186-198.A model for the energy balance of rice fields was improved by using meteorological and geographical data to simulate the changes in the water temperature resulting from plant growth. The average climate of Japan during the period 1971–2000 was used as a baseline. The improved model was used to assess the possible effects of the future climate (2081–2100) on agricultural practices at a spatial resolution of approximately 102km 2 . The most notable result from the simulations is that the water temperature during the growing season for the future climate increased by approximately 1.6–2.002°C throughout the country. This increase can lead to a remarkable northward shift of the isochrones of safe transplanting dates for rice seedlings. This means that the rice cultivation period will be prolonged by approximately 25–30 days. Such an increase in the thermal resources allows greater flexibility of variation in the cropping season as compared with that at present; thus, resulting in a reduction in the frequency of cool summer damage in the northern districts. The area of safe cultivation expands to the northernmost region, if all the forests in the climatically suitable areas can be converted into rice fields. Conversely, climate warming will also induce high-temperature stress in rice plants in one-fifth of the current total cultivation area. The current agricultural practices and rice cultivars used in these areas will inevitably require altering to prevent the projected heat stress during summer.

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[38]
Piao S L, Ciais P, Huang Yet al., 2010. The impacts of climate change on water resources and agriculture in China.Nature, 467: 43-51.China is the world's most populous country and a major emitter of greenhouse gases. Consequently, much research has focused on China's influence on climate change but somewhat less has been written about the impact of climate change on China. China experienced explosive economic growth in recent decades, but with only 7% of the world's arable land available to feed 22% of the world's population, China's economy may be vulnerable to climate change itself. We find, however, that notwithstanding the clear warming that has occurred in China in recent decades, current understanding does not allow a clear assessment of the impact of anthropogenic climate change on China's water resources and agriculture and therefore China's ability to feed its people. To reach a more definitive conclusion, future work must improve regional climate simulations-especially of precipitation-and develop a better understanding of the managed and unmanaged responses of crops to changes in climate, diseases, pests and atmospheric constituents.

DOI PMID

[39]
Prishchepov A V, Müller D, Dubinin Met al., 2013. Determinants of agricultural land abandonment in post-Soviet European Russia.Land Use Policy, 30(1): 873-884.The breakdown of socialism caused massive socio-economic and institutional changes that led to substantial agricultural land abandonment. The goal of our study was to identify the determinants of agricultural land abandonment in post-Soviet Russia during the first decade of transition from a state-controlled economy to a market-driven economy (1990-2000). We analyzed the determinants of agricultural land abandonment for approximately 150,550 km(2) of land area in the provinces (oblasts) of Kaluga, Rjazan. Smolensk, Tula and Vladimir in European Russia. Based on the economic assumptions of profit maximization, we integrated maps of abandoned agricultural land from five similar to 185 km x 185 km Landsat TM/ETM+ footprints with socio-economic, environmental and geographic variables, and we estimated logistic regressions at the pixel level to identify the determinants of agricultural land abandonment. Our results showed that a higher likelihood of agricultural land abandonment was significantly associated with lower average grain yields in the late 1980s and with higher distances from the nearest settlements, municipality centers, and settlements with more than 500 citizens. Hierarchical partitioning showed that the average grain yields in the late 1980s had the greatest power to explain agricultural land abandonment in our models, followed by the locational attributes of the agricultural land. We hypothesize that the termination of 90% of state subsidies for agriculture from 1990 to 2000 was an important underlying cause for the decrease of cultivation in economically and environmentally marginal agriculture areas. Thus, whereas the spatial patterns corresponded to the land rent theory of von Thunen, it was primarily the macro-scale driving forces that fostered agricultural abandonment. Our study highlighted the value of spatially explicit statistical models for studying the determinants of land-use and land-cover change in large areas. (C) 2012 Elsevier Ltd. All rights reserved.

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[40]
Rutten M, van Dijk M, van Rooij Wet al., 2014. Land use dynamics, climate change, and food security in Vietnam: A global-to-local modeling approach.World Development, 59: 29-46.We present an innovative global-to-local modeling approach to analyze impacts of uncertain and complex futures on Vietnam economy via changes in land use patterns. Socio-economic changes are shown to have major implications for the Vietnamese landscape, including natural forest losses with negative consequences for biodiversity and greenhouse gas emissions, and losses of paddy rice and other agricultural lands in the Red River Delta and the Mekong River delta. Climate-related flood risks in these areas further threaten the population, economic assets, and food security. The scenarios reveal the importance of investments in agriculture, land markets, and climate change mitigation and adaptation.

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[41]
Schneeberger N, Bürgi M, Hersperger A Met al., 2007. Driving forces and rates of landscape change as a promising combination for landscape change research: An application on the northern fringe of the Swiss Alps.Land Use Policy, 24(2): 349-361.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">Landscape change is driven by various actors and forces which trigger a specific rate of change. Today, many landscapes change in a direction and with a rate considered unsustainable. Historical insights on actors, driving forces and resulting changes can provide a valuable basis to efficiently control or direct changes. In this paper actors and driving forces of landscape change of the last 120 years are studied in five areas on the northern fringe of the Swiss Alps. Rates of landscape change were reconstructed based on maps. Expert interviews with farmers, politicians, planners and historians as well as historical documents helped in identifying actors and driving forces of the detected landscape change. The contributions of actors and driving forces to landscape change were analyzed by type of driving force (political, economic, cultural, technological and natural/structural). The analysis revealed some key forces, like technological innovations and attitudes and beliefs, operating on several institutional levels and influencing landscape change on a broad basis. Comparing the municipalities disclosed no significant differences regarding the relative contributions of different actors. However, a comparison of the time period before and after World War II revealed distinctive differences in relevant actors and driving forces. Thus, decision-making, policy, and planning must be aware of changing actors and driving forces over time.</p>

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[42]
Schweizer P E, Matlack G R, 2014. Factors driving land use change and forest distribution on the coastal plain of Mississippi, USA.Landscape and Urban Planning, 121: 55-64.Forest distribution is controlled by broad regional trends in land use and by the specific natural and anthropogenic features of a particular site. To separate these influences in landscapes of the Southeastern coastal plain we describe land cover history outside the small city of Hattiesburg, Mississippi. USA, a rural landscape originally occupied by pine savanna and mixed forests. Land cover was recorded at 296 point locations regularly spaced on a 1 km grid. Aerial photographs from 1938. 1958. 1970. 1982. 2000. and 2010 illustrated a progression from open land to pine savanna, Southern Mixed Hardwood Forest (SMF). and built land cover, with low-density residential development encroaching after 1980 鈥 a pattern reflecting broad regional trends in the mid and late 20th century. Examination of point-transitions showed frequent conversion between recent clearcuts and SMF, indicating rapid cycling of small parcels in short rotation forestry, and long-term conversion of abandoned agricultural land to SMF, reflecting regional regrowth following the lumber boom of the early 20th century. Pine savanna declined by introgression of hardwood species rather than by cutting. Logistic regression identified land on floodplains and distant from developed areas as most likely to regenerate as SMF. After 1980 urban expansion was most likely to occur close to existing buildings and arterial roads, suggesting nucleation outside the historical urban core. Thus, modern forest distribution has been decoupled from the natural environmental template. Recent land use changes appear to be driven by proximity to the expanding city rather than regional economic trends.

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[43]
Shi R X, Yang X H, 2010. Research on the climate background in arable land changed areas of China.Journal of Geo-information Science, 12(3): 309-314. (in Chinese)It was very important to learn the climate background in arable land changed areas of China then to estimate food production,to evaluate the balance of arable land quantity and quality,and to put in practice arable land protection and food security.In this paper,the arable land changed areas were firstly analyzed.Next,annual mean temperature,precipitation and sunshine hours were chosen to analyze the climate conditions in arable land changed areas so as to provide suggestions on land protection and economic development.The results showed that annual mean temperature,precipitation and sunshine hours in arable land decreased areas were 0.45-1.05鈩 higher,56.77-79.59mm more and 45.80-98.83h less than arable land increased areas during the four periods from the end of 1980s to 2008.While annual mean temperature,precipitation and sunshine hours in areas with significant arable land reduction were 0.81-1.85鈩 higher,85.69-305.26mm more and 86.96-207.85h less than those with significant arable land increase.The core part of China's arable land was gradually moving upward/northward from the end of 1980s to 2008.According to altitude,the arable land increased areas seemed to be about 0.5-1掳 more northward than the decreased areas,while the significantly increased areas seemed to be about 1-2掳 more northward than the significantly decreased areas.Otherwise,according to latitude,the arable land increased areas seemed to be about 100-200 meters higher than the decreased areas,while the significantly increased areas seemed to be about 150-350 meters higher than the significantly decreased areas.The difference in precipitation became more and more significant between the arable land decreased area and the increased area as time went on from the end of 1980s to 2008.These conclusions are important help for us to learn grain production,to evaluate the balance of arable land quantity and quality,and to readjust industrial layout in China.

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[44]
Shi W J, Tao F L, Liu J Yet al., 2014. Has climate change driven spatio-temporal changes of cropland in Northern China since the 1970s?Climatic Change, 124(1/2): 163-177.Rain; Climate change; Decision making; Reclamation; Water resources

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[45]
Shiferaw A, 2011. Evaluating the land use and land cover dynamics in Borena Woreda of South Wollo Highlands, Ethiopia.Journal of Sustainable Development in Africa, 13(1): 1520-5509.This paper describes the land use and land cover dynamics in Borena02Woreda of South Wollo Highlands of Ethiopia and implications by using02the DPSIR framework(Driving Forces-Pressures-State-Impact-Response) in02a Geographical Information System (GIS) context. The integration of02satellite remote sensing and GIS was an effective approach for analyzing the02direction, rate, and spatial pattern of land use change. Three land use and02land cover maps were produced by analyzing remotely sensed images of02Landsat satellite imageries at three time points (1972,1985,and 2003) . The02result shows five major land use and land cover types. These include forest,02shrub or bush, grassland, agricultural land and bare land. Between (1972 to021985), there was a dramatic expansion of agricultural land followed by bare02land while, shrub land, forest land and grass land showed reduction in02coverage. The period between 1985 to 2003,saw similar changes in02agricultural land, bare land, shrub land and forest land cover but grass land02showed a slight expansion in coverage due to the conversion of forest and02shrub land to grass land. The major driving forces for these changes were02natural factors such as steep slope, drought and Climate change. The human02driving forces for these changes steep slopes, drought and climate change.02The human driving factors include population growth and density, over-use02of land, farm size, land tenure status and land use. These factors exert02pressure and impacts on land use. Implications include biodiversity loss02central ownership of natural resources , the breakdown of traditional02structure and consequent difficulties in the use o fallow lands, open access02to grass lands, inability to protect and manage land resources , inappropriate development strategies and la ck of land use planning. Key words : land use/land cover dynamics, DPSIR model, remote sensing,02Ethiopia.

[46]
Slätmo E, 2011. Driving forces of rural land use change. A review and discussion of the concept ‘driving forces’in landscape research. Occasional Papers 2011: 5 Department of Human and Economic Geography, University of Gothenburg.G02teborgs universitets publikationer (GUP)

[47]
Tang H J, Wu W B, Yang P, 2009. Recent progresses of land use and land cover change models.Acta Geographica Sinica, 64(4): 456-468. (in Chinese)lt;p>Land use and land cover change (LUCC) is a major part and also a main cause of global environmental changes, and it has emerged recently as an important focus for land change studies. Based on the systematic summary of the progress of studies in LUCC in the latest decade, including its theories, methods and applications, a series of problems that should be urgently resolved in the study are put forward, and some important study directions and priorities for future are reviewed. Results show that LUCC model plays an important role and is an efficient tool to support the analysis of the causes, processes and consequences of land use systems and to support land use planning and policy. Second, spatio-temporal patterns of LUCC are the research core of LUCC models. The development of models has experienced an evolvement from single non-spatial to the combination of non-spatial and spatial models, however, at present most models are static models and ignore the temporal dimension of land-use change. Third, feedback is one of the important characteristics of LUCC; however, the majority of the existing LUCC models are very weak in analyzing and presenting the feedbacks of LUCC. In this regard, how to get a better understanding of the feedbacks at different time and space scales will be one of new tasks in LUCC models. Fourth, the objective of LUCC models is to study the dynamic relations of a coupled human-environment. Currently, most LUCC models are partial-equilibrium ones. Future LUCC models will focus on studies on the human-environment system from a systematic and holistic point of view. Fifth, multi-scale analysis in LUCC models is needed for a better understanding of land use change. Early LUCC models used to take a single scale or level of analysis into account. Recently, a number of LUCC models which implement multiple scales can be distinguished. The scaling will be a key issue in future LUCC models. Finally, although many methods of model validation are available, there is not a uniform standard and criterion of model validation. The weakness in reference data also limits the performance of model validation. All these will challenge the development of future LUCC models.</p>

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[48]
Tsakovski S, Astel A, Simeonov V, 2010. Assessment of the water quality of a river catchment by chemometric expertise.Journal of Chemometrics, 24(11/12): 694-702.Abstract The present paper deals with the successive application of self-organizing map (SOM) classification and Hasse diagram technique (HDT) as chemometric tool for assessment of river water quality. The study is carried out by using long-term water quality monitoring data from the Struma River catchment, Bulgaria. The advantages of the SOM algorithm for advanced visualization and classification of large data sets are used for proper selection of chemical parameters being most effective in quality assessment. The proper variable selection combined with some state directives for surface water quality parameters was then used for performing a new data classification separating the objects of interest (sampling sites) into specific patterns. The simultaneous application of the SOM methodology and partial order theory allows to visualize the spatial and temporal evolution of water quality parameters. Thus, it can be seen that the nitrate loads are decreasing with time and the high specificity of a certain sampling station with respect to its water quality data pattern is changing. Copyright 漏 2010 John Wiley & Sons, Ltd.

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[49]
Tubiello F N, Fischer G, 2007. Reducing climate change impacts on agriculture: Global and regional effects of mitigation, 2000-2080.Technological Forecasting and Social Change, 74(7): 1030-1056.What are the implications for agriculture of mitigating greenhouse gas emissions? By when and by how much are impacts reduced? Where does it matter most? We investigated these questions within the new A2 emission scenario, recently developed at the International Institute of Applied Systems Analysis with revised population and gross domestic product projections. Coupling an agro-ecological model to a global food trade model, two distinct sets of climate simulations were analyzed: 1) A non-mitigated scenario, with atmospheric CO 2 concentrations over 800ppm by 2100; and 2) A mitigation scenario , with CO 2 concentrations stabilized at 550ppm by 2100. Impacts of climate change on crop yield were evaluated for the period 1990–2080, then used as input for economic analyses. Key trends were computed over the 21st century for food demand, production and trade, focusing on potential monetary (aggregate value added) and human (risk of hunger) impacts. The results from this study suggested that mitigation could positively impact agriculture. With mitigation, global costs of climate change, though relatively small in absolute amounts, were reduced by 75–100%; and the number of additional people at risk of malnutrition was reduced by 80–95%. Significant geographic and temporal differences were found. Regional effects often diverged from global net results, with some regions worse off under mitigation compared to the unmitigated case.

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[50]
Upton V, O'Donoghue C, Ryan M, 2014. The physical, economic and policy drivers of land conversion to forestry in Ireland.Journal of Environmental Management, 132: 79-86.Land use change is fundamentally a product of the interaction of physical land characteristics, economic considerations and agricultural and environmental policies. Researchers are increasingly combining physical and socio-economic spatial data to investigate the drivers of land-use change in relation to policy and economic developments. Focusing on Ireland, this study develops a panel data set of annual afforestation over 2811 small-area boundaries between 1993 and 2007 from vector and raster data sources. Soil type and other physical characteristics are combined with the net returns of converting agricultural land to forestry, based on the micro-simulation of individual farm incomes, to investigate land conversion. A spatial econometric approach is adopted to model the data and a range of physical, economic and policy factors are identified as having a significant effect on afforestation rates. In addition to the financial returns, the availability and quality of land and the implementation of environmental protection policies are identified as important factors in land conversion. The implications of these factors for the goal of forest expansion are discussed in relation to conflicting current and future land use policies.

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[51]
Wen J Q, Pu L J, Zhang R S, 2011. A spatial econometric analysis on differential changes and driving forces of arable land: A case study of Jiangsu Province.Resources and Environment in the Yangtze Basin, 20(5): 628-634. (in Chinese)<p>Based on the data of arable land in 13 municipal cities in Jiangsu Province from 1998 to 2008,this paper analyzed the change process of arable land and spatial difference nearly 10 years,test the spatial autocorrelation of arable land by the Moran I index,and studied the driving mechanism in the change of arable land by adopting spatial econometric model.From 1998 to 2008,the area of arable land decreased 2969 &times; 104 hm2 with yearly decrease of 270 &times; 104 hm2.There was an obvious spatial difference in the change of arable land.The most serious regions of the reduction of arable land included Wuxi,Suzhou,Changzhou,Nanjing and Zhenjiang city.There existed an obvious spatial correlation of the distribution of arable land in Jiangsu Province.Its value of Moran I increased from 04003 to 04524 from 1998 to 2008，and this correlation was growing stronger.The population,economic growth and the ratio of grain and economic crops were the main driving factors and their elastic coefficients were -0803, -0070 and 0069,respectively.There was an obvious significant spatial diffusion effect in the change of arable land in various factors in adjacent areas,and the elastic coefficient was 0779.Therefore,we should take the spatial correlation into consideration in the future to promote the formation of transregional arable land protection mechanism,and establish an integrative regional protection policy</p>

[52]
Wessels K, Prince S, Malherbe Jet al., 2007. Can human-induced land degradation be distinguished from the effects of rainfall variability? A case study in South Africa.Journal of Arid Environments, 68(2): 271-297.Advanced Very High Resolution Radiometer (AVHRR), Normalized Difference Vegetation Index data (NDVI, 1km 2 , 1985–2003) and modeled net primary production (NPP, 8km 2 , 1981–2000) data were used to estimate vegetation production in South Africa (SA). The linear relationships of Log e Rainfall with NPP and ΣNDVI were calculated for every pixel. Vegetation production generally had a strong relationship with rainfall over most of SA. Therefore, human-induced land degradation can only be detected if its impacts on vegetation production can be distinguished from the effects of rainfall. Two methods were tested (i) Rain-Use Efficiency (RUE=NPP/Rainfall or ΣNDVI/Rainfall) and (ii) Residual Trends (RESTREND), i.e. negative trends in the differences between the observed ΣNDVI and the ΣNDVI predicted by the rainfall. Degraded areas mapped by the National Land Cover in north-eastern SA had reduced RUE; however, annual RUE had a very strong negative correlation with rainfall and varied greatly between years. Therefore, RUE was not a reliable indicator of degradation. The RESTREND method showed promising results at a national scale and in the Limpopo Province, where negative trends were often associated with degraded areas in communal lands. Both positive and negative residual trends can, however, result from natural ecological processes, e.g. the carryover effects of rainfall in previous years. Thus, the RESTREND method can only identify potential problem areas at a regional scale, while the cause of negative trends has to be determined by local investigations.

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[53]
Wu L, Hou X Y, Xu X L, 2014. Analysis of spatical pattern of farmland and its impacting factors in coastal zone of Circum Bohai.Transactions of the Chinese Society of Agricultural Engineering, 30(9): 1-10. (in Chinese)In this paper, coastal zone of Circum Bohai Sea Region which covers an area of approximately 170, 000 km2 was selected as the study area. The spatial distribution characteristics of farmland of this study area were analyzed and the relationship between farmland distribution and natural, social or economic impacting factors was explored. Based on Landsat TM images acquired in 2009/2010, farmland distribution map was created through visual interpretation with auxiliary data in ArcGIS 9.3. Then farmland distribution map was overlaid with a lattice map to statistic area of farmland in each 5 km 脳 5 km lattice. Impacting factors of farmland consisted of elevation, slope, distance to nearest coastline, distance to nearest railway, distance to nearest road, distance to nearest residential area, distance to nearest river, average yearly precipitation, average yearly temperature and population density, which were compiled into raster format data with a spatial resolution of 5 km 脳 5 km and normalized between 0 and 1 in ArcGIS 9.3. As conventional statistical methods assumed that the data to be analyzed was statistically independent, it was inappropriate to use traditional statistical method to analyze spatial land use data which had a tendency to be dependent. In this study, ordinary least square linear regression model(OLS), spatial error model(SEM), spatial lag model(SLM) and geographically weighted regression model(GWR) were established from global and local perspectives. Several evaluation indexes were selected to assess the performance of those models. The results showed that: 1) Farmland was the main land use type, which occupied 53% of the whole study area. Positive spatial autocorrelation that decreased gradually with distance was detected in both farmland distribution and impacting factors; 2) Spatial autoregressive models and GWR had a better goodness-of-fit than conventional linear regression model. As to spatial autoregressive models, SEM performed better than SLM in this study, as was indicated by higher preudo R2 value and maximum likelihood logarithm(LIK) value, and lower Akaike information criterion(AIC) value, Schwartz criterion(SC) value and residuals for the former model; 3) GWR could be used to explore spatial variation in the relations between cultivated land distribution and different impacts factors, providing more detailed information, while SEM could only explore the relations from a global view; 4) The SEM showed a positive correlation between farmland and elevation, slope, distance to the nearest roads, as well as a negative correlation between farmland and distance to nearest shoreline, distance to nearest railroad, distance to nearest settlements, average yearly temperature, average yearly precipitation from a global perspective; and 5)The GWR revealed both positive and negative correlations between farmland and impacting factors(expect for average yearly precipitation). Among the most sensitive factors affecting farmland distribution, average yearly temperature and average yearly precipitation were the main positive factors, while elevation, slope and distance to nearest residential area were the main negative factors.

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[54]
Xie H L, Li B, 2008. Driving forces analysis of land-use pattern changes based on logistic regression model in the farming-pastoral zone: A case study of Ongiud Banner, Inner Mongolia.Geographical Research, 27(2): 294-304. (in Chinese)本文以农牧交错带的典型区域——内蒙古翁牛特旗为例,考虑土地利用变化过程的空间变量,建立了不同土地利用变化过程的logistic回归模型。结果表明:模型中转为耕地的主要解释变量是到农村居民点的距离和农业气候区;转为草地的主要解释变量是到农村居民点的距离、土壤表层有机质含量和到乡镇中心的距离;转为林地的主要解释变量是到农村居民点的距离和海拔;空间异质性和土地利用变化过程的时间变量共同影响着使用logistic回归模型来解释土地利用变化驱动力的能力;通过对草地logistic回归模型的检验,得出空间统计模型能较好地揭示不同土地利用变化过程的主要驱动力及其作用机理。

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[55]
Xu D Y, Kang X W, Liu Z Let al., 2009. Assessing the relative role of climate change and human activities in sandy desertification of Ordos region,China. Science in China. Series D:Earth Sciences, 39(4): 516-528. (in Chinese)Climate change and human activities are driving forces of sandy desertification and the relative role of them in sandy desertification is the hot point in related researches. A study was carried to as

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[56]
Yan D, Schneider U A, Schmid Eet al., 2013. Interactions between land use change, regional development, and climate change in the Poyang Lake district from 1985 to 2035.Agricultural Systems, 119: 10-21.Land use change and climate change are two major global modifications of our environment and are predicted to continue in the future. To assess how climate change affects land use and regional development in the Poyang Lake district in China, we use agent-based modeling and simulate the physical and socio-economic drivers within two interactive sub-models for urban expansion and rural development. The modeling outputs from 1985 to 2005 show good agreement with the observed land use change. Possible land use changes and regional development paths until 2035 are examined for three SRES scenarios including A1B (rapid growth), A2 (regional-diversified growth) and B1 (growth with clean technologies). The results show that climate change induced impacts on land use change and regional development are highly relevant and may even amplify the complex interactions. In particular, cropland, forest, water area, urban, and grassland are more sensitive to these changes than unused land. The more environmental friendly B1 scenario results in less concerning land use changes.

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[57]
Ye Y, Fang X Q, Khan M A U, 2012. Migration and reclamation in northeast China in response to climatic disasters in North China over the past 300 years.Regional Environmental Change, 12(1): 193-206.Climatic disaster-induced migration and its effects on land exploitation of new settlements is a crucial topic that needs to be researched to better understand the impact of climate change and human adaptation. This paper focuses on the process and mechanism of migrant–reclamation in Northeast China in response to climatic disasters over the past 30002years. The research used comparative analysis of key interlinked factors in this response involving drought/flood events, population, cropland area, farmer revolts, administrations establishment, and land reclamation policies. It draws the following conclusions: (1) seven peaks of migrants–reclamation in Northeast China were evident, most likely when frequent climatic disasters happened in North China, such as the drought–flood in 1851–1859, drought in 1875–1877, and drought 1927–1929; (2) six instances of policy transformation adopted to cope with extreme climatic events, including distinctive examples like changing to a firm policy prohibiting migration in 1740 and a subsequent lifting of that prohibition in 1860; and (3) the fast expansion of the northern agricultural boundary since the middle of the nineteenth century in this area benefited from a climate change trend from a cold period into a warm period. Altogether, over the past 30002years, extreme climatic disasters in North China have deepened the contradiction between the limited land resources and the rapidly increasing population and have resulted in migration and reclamation in Northeast China. Climate, policy, and reclamation constructed an organic chain of response that dominated the land use/cover change process of Northeast China.

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[58]
Yin R S, Xiang Q, Xu J Tet al., 2010. Modeling the driving forces of the land use and land cover changes along the Upper Yangtze River of China.Environmental Management, 45(3): 454-465.Induced by high population density, rapid but uneven economic growth, and historic resource exploitation, China鈥檚 upper Yangtze basin has witnessed remarkable changes in land use and cover, which have resulted in severe environmental consequences, such as flooding, soil erosion, and habitat loss. This article examines the causes of land use and land cover change (LUCC) along the Jinsha River, one primary section of the upper Yangtze, aiming to better understand the human impact on the dynamic LUCC process and to support necessary policy actions for more sustainable land use and environmental protection. Using a repeated cross-sectional dataset covering 31 counties over four time periods from 1975 to 2000, we develop a fractional logit model to empirically determine the effects of socioeconomic and institutional factors on changes for cropland, forestland, and grassland. It is shown that population expansion, food self-sufficiency, and better market access drove cropland expansion, while industrial development contributed significantly to the increase of forestland and the decrease of other land uses. Similarly, stable tenure had a positive effect on forest protection. Moreover, past land use decisions were less significantly influenced by distorted market signals. We believe that these and other findings carry important policy implications.

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[59]
Yu Q Y, Wu W B, Tang H Jet al., 2011. Complex system theory and agent-based modeling: Progress in land change science.Acta Geographica Sinica, 66(11): 1518-1530. (in Chinese)Based on complex system theory and agent-based models (ABMs), the paper summarizes recent progresses in land change science from the perspective of theory and methodology respectively. Complex system theory is the theoretical basis for carrying out researches on the complex land change issues in the &quot;coupled human and natural systems&quot;; while ABMs, one of the key tools for complex system studies, introduces innovating perspective to traditional land change modeling. The integration of ABMs and land use/cover change models (ABM/LUCC) has achieved several important breakthroughs recently; however, some of the crucial issues remain unsolved, such as the problem of &quot;theory divorced from practice&quot; and the deficiency in cross-site comparison studies. As for current problems, the authors finally have discussion and draw a conclusion that firstly, ABMs should be constructed by the support of complex system theory. Moreover, the natural ABM/LUCC are supposed to explore the comprehensive human-natural interactions in land systems, to predict land system dynamics, and to analyze the consequences of land system change.

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[60]
Yu Q Y, Wu W B, Yang Pet al., 2013. Progress of agent-based agriculture land change modeling: A review.Acta Ecologica Sinica, 33(6): 1690-1700. (in Chinese)Agricultural land use and land cover change(Agri-LUCC) is one of the key issues among global change and sustainability studies.Year-on-year progress makes "agricultural land change" to be an emerging interdisciplinary science.As an effective tool for understanding the driver,process and consequence of Agri-LUCC,spatially-explicit land change models have successfully applied in representing agricultural landscapes and its possible developments across scales.Although several breakthroughs have been achieved by traditional land change modeling,there are still many crucial issues remain unsolved,especially the insufficient cognition on the complexity and dynamics of agricultural land systems.Recently,some researchers begin to combine agent-based models(ABM,one of the key tools for complex system studies) with land change models,bringing a new emergence of model series in the agricultural land change modeling community,which are called as Agri-ABM/LUCCs.Progress in this field can be summarized as:(1) Based on the complexity system theory,most of these models bring theoretical and methodological innovations in analyzing the complexity of agricultural land systems.(2) These models innovatively take land use decisions at individual level into consideration,based on which to recognize the role of decision makers bringing about changes,through their choices,on regional level landscapes.Such "modeling with stakeholders" underlines the role of farmers in agricultural transformation,facilitating the expression of diversified decisions on agricultural land use from heterogeneous farmers.(3) Agri-ABM/LUCC links "land change driving forces" with "land use consequences" as an endogenous feedback loop in agricultural land change processes.This tightly coupled method describes a better feature of agricultural land dynamics,which is essential for analyzing the vulnerabilities,impacts,and adaptation in agricultural land change context.(4) From the recent literature,a wild range of issues related to farmer鈥瞫 decisions on their land were discussed,including deforestation,agricultural expansion,crop allocation,resource management,and settlement and livelihood decisions.In these studies,various methods and approaches were used in representing farmer鈥瞫 decisions.Methods include linear programming model,optimization model,heuristic imitative and innovative decision-making algorithms,utility function,decision tree,evolutionary programming,probabilistic method,participatory modeling,role playing game,bounded-rational approach,spatial multi-nominal logistic functions,among others.(5) This new perspective provides a way to dynamically link agricultural land change assessments for integrated human-natural studies.On one hand,consequence of agricultural land change can be used to forecast crop production then to develop food security scenarios;on the other hand,the same land change result is valuable for predicting carbon-nitrogen cycling processes,consequently for projecting carbon sequestration within large scale agricultural landscapes.Scenarios of food and ecological security provide feedbacks to individual farmers to alter their decisions of land use in turn.Beside the progress,however,problems of current Agri-ABM/LUCCs still exist,such as "theory divorced from practice",deficiency in cross-site comparison,and difficulties in carrying out large-scale modeling.The most critical problem is that other than the common characteristics of complex adaptive systems,some of the special features of agricultural land systems exit in their spatial-temporal dynamics,scaling effects,coupled human and natural issues,and multi-dimension feedbacks.These features are still not well examined in the current studies,which require further in-depth discussions in the future.

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[61]
Zhan J Y, Shi N N, Deng X Z, 2010. Driving mechanism of cultivated land conversions in Jiangxi.Acta Geographica Sinica, 65(4): 485-493. (in Chinese)Cultivated land conversion is considered to be one of the major problems threatening agricultural development,food security and supporting functions of cultivated land conversions in Jiangxi.Recognization on the driving mechanism of cultivated land conversions is far from complete.This paper integrates geophysical information with socio-economic processes and policy changes to build an econometric model to explore the driving mechanism of cultivated land conversion from 1988 to 2005.Three equations are included in the econometric model to uncover the driving mechanism for agriculture production process,the conversion process from cultivated land to built-up area and the conversion process from cultivated land to forested land/grassland,which includes explained variables to identify the three main processes and the driving forces from social-economic,demographic,geophysical,managerial domains.The estimation results from the econometric model shows that the population size is one of the predominant driver for the three processes,and socio-economic factors play a decisive role in a short period of time.The synthesized influences from the multiple driving factors produces an enhanced or offsetting resultant effects for the ecological processes.The research results show that the agricultural population size and investment are the key influencing factors for agricultural production,while population size,the ratio of the plains area to total land and land management policy are the key explanatory variables in the conversion process of cultivated land to built-up area;and the proportion of agricultural population,terrain slope,food production and non-agricultural industry are the important influencing variables in the process of conversion from cultivated land to forested land/grassland.

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[62]
Zhang H D, Yu D S, Shi X Zet al., 2010. Dynamics of recent cultivated land in Zhejiang Province and relevant driving factors.Chinese Journal of Applied Ecology, 21(12): 3120-3126. (in Chinese)Through the human-computer interactive interpretation of the 2000,2005,and 2008 remote sensing images of Zhejiang Province with the help of RS and GIS techniques,the dynamic database of cultivated land change in the province in 2000-2008 was established,and the driving factors of the cultivated land change were analyzed by ridge regression analysis. There was a notable cultivated land change in the province in 2000 -2008. In 2000 -2005 and 2005-2008,the annual cultivated land change in the province arrived -1. 42% and -1. 46% ,respectively,and most of the cultivated land was changed into residential and industrial land. Non-agricultural population rate,real estate investment,urban green area,and orchard area were thought to be the main driving factors of the cultivated land change in Zhejiang Province,and even,in the developed areas of east China.

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[63]
Zhang L Q, Chen F K, 2014. Analysis and forecast on prospect about influence of urbanization gradual progress on cultivated land in China based on Logistic model.Transactions of the Chinese Society of Agricultural Engineering, 30(4): 1-11. (in Chinese)Abstract: Accelerating urbanization is a major development strategy proposed by the 18th Communist Party of China (CPC) National Congress. The prospect for the influence of China's future urbanization evolution on cultivated land relates to the realization of a red line target of cultivated land protection and food safety. To reveal the evolution of China's urbanization impact on cultivated land, the author explores the problem based on a logistic model. Using SPSS software and China's urbanization level data from 1978 to 2011, the goodness-of-fit maximum estimation method of regression curve was employed to estimate the saturation value of China's urbanization level and the Logistic model of describing China's urbanization evolution was structured. Accordingly, the development level of China's future urbanization was predicted. Based on a STRIPAT model and relevant data of China 's eco-social development from 1996 to 2011, SPSS software was combined with a partial least squares regression method to reveal the marginal contributions of urbanization process, population, economic development level, and technical factors on cultivated land change. According to China's future urbanization evolutionary trend and the marginal influence of urbanization on cultivated land, the influence of China's future urbanization on cultivated land was measured. The results are shown as the followings：1）The saturation value of China's urbanization level is 83%. 2） China's urbanization level will reach 57.68% and 65.73% in 2020 and 2030 respectively. Before 2020, the annual average growth rate of urbanization will be 0.97 percent point, and from 2020 to 2030, that will be 0.81percent point. 3） The marginal elasticity coefficient of urbanization, population, economic development level, and technological factors on cultivated land change will be ?0.007391, ?0.007133, ?0.009343, and?0.002952 respectively. 4）From 2012 to 2020, urbanization evolution will lead to a net area reduction of cultivated land of 13.81×104hm2 with an annual average reduction of 1.53×104 hm2. From 2020 to 2030, that will be 10.87×104 hm2 with an annual average reduction of 1.09×104 hm2. Based on the results of the above, several measures should be implemented including focusing on the quality of urbanization with a moderate grasp on the speed of urbanization, scientifically preparing the annual land supply planning, adopting a differentiated land supply strategy, abandoning the wrong philosophy of land finance, strictly implementing national protection policies foe cultivated land, rigorously controlling real estate land and low-level or repeated production land; severely punishing violations of land use, and strengthening the policy recommendations on land supervision. The results can provide a reference for management to grasp the moderate urbanization pace and rhythm, scientifically prepare a land supply plan, and formulate cultivated land protection policies, as well as offer a method of reference for similar studies on a provincial scale.

[64]
Zhang Y, Li X B, Song W, 2014. Determinants of cropland abandonment at the parcel, household and village levels in mountain areas of China: A multi-level analysis.Land Use Policy, 41: 186-192.Cropland abandonment accompanying economic development has been observed worldwide. China has experienced a large amount of land abandonment in recent years. However, the reasons for it are not entirely clear. Although abandonment decisions are made by individual households, the underlying conditions reflect processes operating at multiple levels. Therefore, we aimed to detect the influences on land abandonment at the parcel, household and village levels. We developed and employed a multi-level statistical model using farm household survey data and geographical maps of Wulong County. Our model revealed that of the variance in occurrence of land parcel abandonment, 7% and 13% can be explained at the household and village levels, respectively, while the remnant 80% can be explained at the land parcel features itself. We found that land abandonment is more prone to occur on parcels that are on steep slopes, have poor quality soil, or are remote from the laborers鈥 residences. Households with less agricultural labor per unit land area showed a high probability of land abandonment. We also found a nonlinear influence of labor age on land abandonment, with households comprising middle-aged laborers having a low land abandonment probability. Parcels in villages with high elevation, far from the county administrative center or with low prevalence of leased land are inclined to abandonment. We also found, surprisingly, that the household proportion of males among its agricultural laborers did not significantly influence the occurrence of land abandonment at the parcel level, probably due to the male agricultural laborers being overwhelmingly old (average age greater than 56 years). To alleviate land abandonment, we suggest improving land tenure and transfer security to ensure stable access to the land rental market, and also improving infrastructure in remote regions.

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[65]
Zhong T Y, Huang X J, Zhang X Yet al., 2011. Temporal and spatial variability of agricultural land loss in relation to policy and accessibility in a low hilly region of Southeast China.Land Use Policy, 28(4): 762-769.The temporal and spatial patterns in land use change in a low hilly region in southeast of China was analyzed from maps of converted agricultural land for 1999-2006. The factors driving farm land conversion was analyzed using logistic regression models. The amount of agricultural land loss varied temporally, and the spatial distribution of converted agricultural land patches decreased from low to high altitudes in the study area. Analysis using logistic regression models showed that good accessibility sped up the conversion of agricultural land to other uses, the elevation of a parcel lowered the risk of conversion, and agricultural land conversions are highly correlated with its adjacent or neighboring parcels' land use, with the probability of being converted decreasing as the distance to nearest construction land increases. In addition, land use policy, especially the land use regulation policy issued by the central government decreased the agricultural land loss, and the more stringent regulation on cultivated land conversion lowered the possibility of conversion from cultivated land to other use. (C) 2011 Elsevier Ltd. All rights reserved.

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